Engineering Analytics

Best Practices of CI/CD Optimization Using DORA Metrics

Every delay in your deployment could mean losing a customer. Speed and reliability are crucial, yet many teams struggle with slow deployment cycles, frustrating rollbacks, and poor visibility into performance metrics.

When you’ve worked hard on a feature, it is frustrating when a last-minute bug derails the deployment. Or you face a rollback that disrupts workflows and undermines team confidence. These familiar scenarios breed anxiety and inefficiency, impacting team dynamics and business outcomes.

Fortunately, DORA metrics offer a practical framework to address these challenges. By leveraging these metrics, organizations can gain insights into their CI/CD practices, pinpoint areas for improvement, and cultivate a culture of accountability. This blog will explore how to optimize CI/CD processes using DORA metrics, providing best practices and actionable strategies to help teams deliver quality software faster and more reliably.

Understanding the challenges in CI/CD optimization

Before we dive into solutions, it’s important to recognize the common challenges teams face in CI/CD optimization. By understanding these issues, we can better appreciate the strategies needed to overcome them.

Slow deployment cycles

Development teams frequently experience slow deployment cycles due to a variety of factors, including complex code bases, inadequate testing, and manual processes. Each of these elements can create significant bottlenecks. A sluggish cycle not only hampers agility but also reduces responsiveness to customer needs and market changes. To address this, teams can adopt practices like:

  • Streamlining the pipeline: Evaluate each step in your deployment pipeline to identify redundancies or unnecessary manual interventions. Aim to automate where possible.
  • Using feature flags: Implement feature toggles to enable or disable features without deploying new code. This allows you to deploy more frequently while managing risk effectively.

Frequent rollbacks

Frequent rollbacks can significantly disrupt workflows and erode team confidence. They typically indicate issues such as inadequate testing, lack of integration processes, or insufficient quality assurance. To mitigate this:

  • Enhance testing practices: Invest in automated testing at all levels—unit, integration, and end-to-end testing. This ensures that issues are caught early in the development process.
  • Implement a staging environment: Conduct final tests before deployment, use a staging environment that mirrors production. This practice helps catch integration issues that might not appear in earlier testing phases.

Visibility gaps

A lack of visibility into your CI/CD pipeline can make it challenging to track performance and pinpoint areas for improvement. This opacity can lead to delays and hinder your ability to make data-driven decisions. To improve visibility:

  • Adopt dashboard tools: Use dashboards that visualize key metrics in real time, allowing teams to monitor the health of the CI/CD pipeline effectively.
  • Regularly review performance: Schedule consistent review meetings to discuss metrics, successes, and areas for improvement. This fosters a culture of transparency and accountability.

Cultural barriers

Cultural barriers between development and operations teams can lead to misunderstandings and inefficiencies. To foster a more collaborative environment:

  • Encourage cross-team collaboration: Hold regular meetings that bring developers and operations staff together to discuss challenges and share knowledge.
  • Cultivate a DevOps mindset: Promote the principles of DevOps across your organization to break down silos and encourage shared responsibility for software delivery.

We understand how these challenges can create stress and hinder your team’s well-being. Addressing them is crucial not just for project success but also for maintaining a positive and productive work environment.

Introduction to DORA metrics

DORA (DevOps Research and Assessment) metrics are key performance indicators that provide valuable insights into your software delivery performance. They help measure and improve the effectiveness of your CI/CD practices, making them crucial for software teams aiming for excellence.

Overview of the four key metrics

  • Deployment frequency: This metric indicates how often code is successfully deployed to production. High deployment frequency shows a responsive and agile team.
  • Lead time for changes: This measures the time it takes for code to go from committed to deployed in production. Short lead times indicate efficient processes and quick feedback loops.
  • Change failure rate: This tracks the percentage of deployments that lead to failures in production. A lower change failure rate reflects higher code quality and effective testing practices.
  • Mean time to recovery (MTTR): This metric assesses how quickly the team can restore service after a failure. A shorter MTTR indicates a resilient system and effective incident management practices.

By understanding and utilizing these metrics, software teams gain actionable insights that foster continuous improvement and a culture of accountability.

Best practices for CI/CD optimization using DORA metrics

Implementing best practices is crucial for optimizing your CI/CD processes. Each practice provides actionable insights that can lead to substantial improvements.

Measure and analyze current performance

To effectively measure and analyze your current performance, start by utilizing the right tools to gather valuable data. This foundational step is essential for identifying areas that need improvement.

  • Utilize tools: Use tools like GitLab, Jenkins, and Typo to collect and visualize data on your DORA metrics. This data forms a solid foundation for identifying performance gaps.
  • Conduct regular performance reviews: Regularly review performance to pinpoint bottlenecks and areas needing improvement. A data-driven approach can reveal insights that may not be immediately obvious.
  • Establish baseline metrics: Set baseline metrics to understand your current performance, allowing you to set realistic improvement targets.

How Typo helps: Typo seamlessly integrates with your CI/CD tools, offering real-time insights into DORA metrics. This integration simplifies assessment and helps identify specific areas for enhancement.

Set specific, measurable goals

Clearly defined goals are crucial for driving performance. Establishing specific, measurable goals aligns your team's efforts with broader organizational objectives.

  • Define SMART goals: Establish goals that are Specific, Measurable, Achievable, Relevant, and Time-bound (SMART) aligned with your DORA metrics to ensure clarity in your objectives.
  • Communicate goals clearly: Ensure that these goals are communicated effectively to all team members. Utilize project management tools like ClickUp to track progress and maintain accountability.
  • Align with business goals: Align your objectives with broader business goals to support overall company strategy, reinforcing the importance of each team member's contribution.

How Typo helps: Typo's goal-setting and tracking capabilities promote accountability within your team, helping monitor progress toward targets and keeping everyone aligned and focused.

Implement incremental changes

Implementing gradual changes based on data insights can lead to more sustainable improvements. Focusing on small, manageable changes can often yield better results than sweeping overhauls.

  • Introduce gradual improvements: Focus on small, achievable changes based on insights from your DORA metrics. This approach is often more effective than trying to overhaul the entire system at once.
  • Enhance automation and testing: Work on enhancing automation and testing processes to reduce lead times and failure rates. Continuous integration practices should include automated unit and integration tests.
  • Incorporate continuous testing: Implement a CI/CD pipeline that includes continuous testing. By catching issues early, teams can significantly reduce lead times and minimize the impact of failures.

How Typo helps: Typo provides actionable recommendations based on performance data, guiding teams through effective process changes that can be implemented incrementally.

Foster a culture of collaboration

A collaborative environment fosters innovation and efficiency. Encouraging open communication and shared responsibility can significantly enhance team dynamics.

  • Encourage open communication: Promote transparent communication among team members using tools like Slack or Microsoft Teams.
  • Utilize retrospectives: Regularly hold retrospectives to celebrate successes and learn collectively from setbacks. This practice can improve team dynamics and help identify areas for improvement.
  • Promote cross-functional collaboration: Foster collaboration between development and operations teams. Conduct joint planning sessions to ensure alignment on objectives and priorities.

How Typo helps: With features like shared dashboards and performance reports, Typo facilitates transparency and alignment, breaking down silos and ensuring everyone is on the same page.

Review and adapt regularly

Regular reviews are essential for maintaining momentum and ensuring alignment with goals. Establishing a routine for evaluation can help your team adapt to changes effectively.

  • Establish a routine: Create a routine for evaluating your DORA metrics and adjusting strategies accordingly. Regular check-ins help ensure that your team remains aligned with its goals.
  • Conduct retrospectives: Use retrospectives to gather insights and continuously improve processes. Cultivate a safe environment where team members can express concerns and suggest improvements.
  • Consider A/B testing: Implement A/B testing in your CI/CD process to measure effectiveness. Testing different approaches can help identify the most effective practices.

How Typo helps: Typo’s advanced analytics capabilities support in-depth reviews, making it easier to identify trends and adapt your strategies effectively. This ongoing evaluation is key to maintaining momentum and achieving long-term success.

Additional strategies for faster deployments

To enhance your CI/CD process and achieve faster deployments, consider implementing the following strategies:

Automation

Automate various aspects of the development lifecycle to improve efficiency. For build automation, utilize tools like Jenkins, GitLab CI/CD, or CircleCI to streamline the process of building applications from source code. This reduces errors and increases speed. Implementing automated unit, integration, and regression tests allows teams to catch defects early in the development process, significantly reducing the time spent on manual testing and enhancing code quality. 

Additionally, automate the deployment of applications to different environments (development, staging, production) using tools like Ansible, Puppet, or Chef to ensure consistency and minimize the risk of human error during deployments.

Version Control

Employ a version control system like Git to effectively track changes to your codebase and facilitate collaboration among developers. Implementing effective branching strategies such as Gitflow or GitHub Flow helps manage different versions of your code and isolate development work, allowing multiple team members to work on features simultaneously without conflicts.

Continuous Integration

Encourage developers to commit their code changes frequently to the main branch. This practice helps reduce integration issues and allows conflicts to be identified early. Set up automated builds and tests that run whenever new code is committed to the main branch. 

This ensures that issues are caught immediately, allowing for quicker resolutions. Providing developers with immediate feedback on the success or failure of their builds and tests fosters a culture of accountability and promotes continuous improvement.

Continuous Delivery

Automate the deployment of applications to various environments, which reduces manual effort and minimizes the potential for errors. Ensure consistency between different environments to minimize deployment risks; utilizing containers or virtualization can help achieve this. 

Additionally, consider implementing canary releases, where new features are gradually rolled out to a small subset of users before a full deployment. This allows teams to monitor performance and address any issues before they impact the entire user base.

Infrastructure as Code (IaC)

Use tools like Terraform or CloudFormation to manage infrastructure resources (e.g., servers, networks, storage) as code. This approach simplifies infrastructure management and enhances consistency across environments. Store infrastructure code in a version control system to track changes and facilitate collaboration. 

This practice enables teams to maintain a history of infrastructure changes and revert if necessary. Ensuring consistent infrastructure across different environments through IaC reduces discrepancies that can lead to deployment failures.

Monitoring and Feedback

Implement monitoring tools to track the performance and health of your applications in production. Continuous monitoring allows teams to proactively identify and resolve issues before they escalate. Set up automated alerts to notify teams of critical issues or performance degradation. 

Quick alerts enable faster responses to potential problems. Use feedback from monitoring and alerting systems to identify and address problems proactively, helping teams learn from past deployments and improve future processes.

Final thoughts

By implementing these best practices, you will improve your deployment speed and reliability while also boosting team satisfaction and delivering better experiences to your customers. Remember, you’re not alone on this journey—resources and communities are available to support you every step of the way.

Your best bet for seamless collaboration is with Typo, sign up for a personalized demo and find out yourself! 

Tracking DORA Metrics for Mobile Apps

Mobile development comes with a unique set of challenges: rapid release cycles, stringent user expectations, and the complexities of maintaining quality across diverse devices and operating systems. Engineering teams need robust frameworks to measure their performance and optimize their development processes effectively. 

DORA metrics—Deployment Frequency, Lead Time for Changes, Mean Time to Recovery (MTTR), and Change Failure Rate—are key indicators that provide valuable insights into a team’s DevOps performance. Leveraging these metrics can empower mobile development teams to make data-driven improvements that boost efficiency and enhance user satisfaction.

Importance of DORA Metrics in Mobile Development

DORA metrics, rooted in research from the DevOps Research and Assessment (DORA) group, help teams measure key aspects of software delivery performance.

Here's why they matter for mobile development:

  • Deployment Frequency: Mobile teams need to keep up with the fast pace of updates required to satisfy user demand. Frequent, smooth deployments signal a team’s ability to deliver features, fixes, and updates consistently.
  • Lead Time for Changes: This metric tracks the time between code commit and deployment. For mobile teams, shorter lead times mean a streamlined process, allowing quicker responses to user feedback and faster feature rollouts.
  • MTTR: Downtime in mobile apps can result in frustrated users and poor reviews. By tracking MTTR, teams can assess and improve their incident response processes, minimizing the time an app remains in a broken state.
  • Change Failure Rate: A high change failure rate can indicate inadequate testing or rushed releases. Monitoring this helps mobile teams enhance their quality assurance practices and prevent issues from reaching production.

Deep Dive into Practical Solutions for Tracking DORA Metrics

Tracking DORA metrics in mobile app development involves a range of technical strategies. Here, we explore practical approaches to implement effective measurement and visualization of these metrics.

Implementing a Measurement Framework

Integrating DORA metrics into existing workflows requires more than a simple add-on; it demands technical adjustments and robust toolchains that support continuous data collection and analysis.

  1. Automated Data Collection

Automating the collection of DORA metrics starts with choosing the right CI/CD platforms and tools that align with mobile development. Popular options include:

  • Jenkins Pipelines: Set up custom pipeline scripts that log deployment events and timestamps, capturing deployment frequency and lead times. Use plugins like the Pipeline Stage View for visual insights.
  • GitLab CI/CD: With GitLab's built-in analytics, teams can monitor deployment frequency and lead time for changes directly within their CI/CD pipeline.
  • GitHub Actions: Utilize workflows that trigger on commits and deployments. Custom actions can be developed to log data and push it to external observability platforms for visualization.

Technical setup: For accurate deployment tracking, implement triggers in your CI/CD pipelines that capture key timestamps at each stage (e.g., start and end of builds, start of deployment). This can be done using shell scripts that append timestamps to a database or monitoring tool.

  1. Real-Time Monitoring and Visualization

To make sense of the collected data, teams need a robust visualization strategy. Here’s a deeper look at setting up effective dashboards:

  • Prometheus with Grafana: Integrate Prometheus to scrape data from CI/CD pipelines, and use Grafana to create dashboards with deployment trends and lead time breakdowns.
  • Elastic Stack (ELK): Ship logs from your CI/CD process to Elasticsearch and build visualizations in Kibana. This setup provides detailed logs alongside high-level metrics.

Technical Implementation Tips:

  • Use Prometheus exporters or custom scripts that expose metric data as HTTP endpoints.
  • Design Grafana dashboards to show current and historical trends for DORA metrics, using panels that highlight anomalies or spikes in lead time or failure rates.

  1. Comprehensive Testing Pipelines

Testing is integral to maintaining a low change failure rate. To align with this, engineering teams should develop thorough, automated testing strategies:

  • Unit Testing: Implement unit tests with frameworks like JUnit for Android or XCTest for iOS. Ensure these are part of every build to catch low-level issues early.
  • Integration Testing: Use tools such as Espresso and UIAutomator for Android and XCUITest for iOS to validate complex user interactions and integrations.
  • End-to-End Testing: Integrate Appium or Selenium to automate tests across different devices and OS versions. End-to-end testing helps simulate real-world usage and ensures new deployments don't break critical app flows.

Pipeline Integration:

  • Set up your CI/CD pipeline to trigger these tests automatically post-build. Configure your pipeline to fail early if a test doesn’t pass, preventing faulty code from being deployed.
  1. Incident Response and MTTR Management

Reducing MTTR requires visibility into incidents and the ability to act swiftly. Engineering teams should:

  • Implement Monitoring Tools: Use tools like Firebase Crashlytics for crash reporting and monitoring. Integrate with third-party tools like Sentry for comprehensive error tracking.
  • Set Up Automated Alerts: Configure alerts for critical failures using observability tools like Grafana Loki, Prometheus Alertmanager, or PagerDuty. This ensures that the team is notified as soon as an issue arises.

Strategies for Quick Recovery:

  • Implement automatic rollback procedures using feature flags and deployment strategies such as blue-green deployments or canary releases.
  • Use scripts or custom CI/CD logic to switch between versions if a critical incident is detected.

Weaving Typo into Your Workflow

After implementing these technical solutions, teams can leverage Typo for seamless DORA metrics integration. Typo can help consolidate data and make metric tracking more efficient and less time-consuming.

For teams looking to streamline the integration of DORA metrics tracking, Typo offers a solution that is both powerful and easy to adopt. Typo provides:

  • Automated Deployment Tracking: By integrating with existing CI/CD tools, Typo collects deployment data and visualizes trends, simplifying the tracking of deployment frequency.
  • Detailed Lead Time Analysis: Typo’s analytics engine breaks down lead times by stages in your pipeline, helping teams pinpoint delays in specific steps, such as code review or testing.
  • Real-Time Incident Response Support: Typo includes incident monitoring capabilities that assist in tracking MTTR and offering insights into incident trends, facilitating better response strategies.
  • Seamless Integration: Typo connects effortlessly with platforms like Jenkins, GitLab, GitHub, and Jira, centralizing DORA metrics in one place without disrupting existing workflows.

Typo’s integration capabilities mean engineering teams don’t need to build custom scripts or additional data pipelines. With Typo, developers can focus on analyzing data rather than collecting it, ultimately accelerating their journey toward continuous improvement.

Establishing a Continuous Improvement Cycle

To fully leverage DORA metrics, teams must establish a feedback loop that drives continuous improvement. This section outlines how to create a process that ensures long-term optimization and alignment with development goals.

  1. Regular Data Reviews: Conduct data-driven retrospectives to analyze trends and set goals for improvements.
  2. Iterative Process Enhancements: Use findings to adjust coding practices, enhance automated testing coverage, or refine build processes.
  3. Team Collaboration and Learning: Share knowledge across teams to spread best practices and avoid repeating mistakes.

Empowering Your Mobile Development Process

DORA metrics provide mobile engineering teams with the tools needed to measure and optimize their development processes, enhancing their ability to release high-quality apps efficiently. By integrating DORA metrics tracking through automated data collection, real-time monitoring, comprehensive testing pipelines, and advanced incident response practices, teams can achieve continuous improvement. 

Tools like Typo make these practices even more effective by offering seamless integration and real-time insights, allowing developers to focus on innovation and delivering exceptional user experiences.

Top 5 JIRA Metrics to Boost Productivity

For agile teams, tracking productivity can quickly become overwhelming, especially when too many metrics clutter the process. Many teams feel they’re working hard without seeing the progress they expect. By focusing on a handful of high-impact JIRA metrics, teams can gain clear, actionable insights that streamline decision-making and help them stay on course. 

These five essential metrics highlight what truly drives productivity, enabling teams to make informed adjustments that propel their work forward.

Why JIRA Metrics Matter for Agile Teams

Agile teams often face missed deadlines, unclear priorities, and resource management issues. Without effective metrics, these issues remain hidden, leading to frustration. JIRA metrics provide clarity on team performance, enabling early identification of bottlenecks and allowing teams to stay agile and efficient. By tracking just a few high-impact metrics, teams can make informed, data-driven decisions that improve workflows and outcomes.

Top 5 JIRA Metrics to Improve Your Team’s Productivity

1. Work In Progress (WIP)

Work In Progress (WIP) measures the number of tasks actively being worked on. Setting WIP limits encourages teams to complete existing tasks before starting new ones, which reduces task-switching, increases focus, and improves overall workflow efficiency.

Technical applications: 

Setting WIP limits: On JIRA Kanban boards, teams can set WIP limits for each stage, like “In Progress” or “Review.” This prevents overloading and helps teams maintain steady productivity without overwhelming team members.

Identifying bottlenecks: WIP metrics highlight bottlenecks in real time. If tasks accumulate in a specific stage (e.g., “In Review”), it signals a need to address delays, such as availability of reviewers or unclear review standards.

Using cumulative flow diagrams: JIRA’s cumulative flow diagrams visualize WIP across stages, showing where tasks are getting stuck and helping teams keep workflows balanced.

2. Work Breakdown

Work Breakdown details how tasks are distributed across project components, priorities, and team members. Breaking down tasks into manageable parts (Epics, Stories, Subtasks) provides clarity on resource allocation and ensures each project aspect receives adequate attention.

Technical applications:

Epics and stories in JIRA: JIRA enables teams to organize large projects by breaking them into Epics, Stories, and Subtasks, making complex tasks more manageable and easier to track.

Advanced roadmaps: JIRA’s Advanced Roadmaps allow visualization of task breakdown in a timeline, displaying dependencies and resource allocations. This overview helps maintain balanced workloads across project components.

Tracking priority and status: Custom filters in JIRA allow teams to view high-priority tasks across Epics and Stories, ensuring critical items are progressing as expected.

3. Developer Workload

Developer Workload monitors the task volume and complexity assigned to each developer. This metric ensures balanced workload distribution, preventing burnout and optimizing each developer’s capacity.

Technical applications:

JIRA workload reports: Workload reports aggregate task counts, hours estimated, and priority levels for each developer. This helps project managers reallocate tasks if certain team members are overloaded.

Time tracking and estimation: JIRA allows developers to log actual time spent on tasks, making it possible to compare against estimates for improved workload planning.

Capacity-based assignment: Project managers can analyze workload data to assign tasks based on each developer’s availability and capacity, ensuring sustainable productivity.

4. Team Velocity

Team Velocity measures the amount of work completed in each sprint, establishing a baseline for sprint planning and setting realistic goals.

Technical applications:

Velocity chart: JIRA’s Velocity Chart displays work completed versus planned work, helping teams gauge their performance trends and establish realistic goals for future sprints.

Estimating story points: Story points assigned to tasks allow teams to calculate velocity and capacity more accurately, improving sprint planning and goal setting.

Historical analysis for planning: Historical velocity data enables teams to look back at performance trends, helping identify factors that impacted past sprints and optimizing future planning.

5. Cycle Time

Cycle Time tracks how long tasks take from start to completion, highlighting process inefficiencies. Shorter cycle times generally mean faster delivery.

Technical applications:

Control chart: The Control Chart in JIRA visualizes Cycle Time, displaying how long tasks spend in each stage, helping to identify where delays occur.

Custom workflows and time tracking: Customizable workflows allow teams to assign specific time limits to each stage, identifying areas for improvement and reducing Cycle Time.

SLAs for timely completion: For teams with service-level agreements, setting cycle-time goals can help track SLA adherence, providing benchmarks for performance.

How to Set Up JIRA Metrics for Success: Practical Tips for Maximizing the Benefits of JIRA Metrics with Typo

Effectively setting up and using JIRA metrics requires strategic configuration and the right tools to turn raw data into actionable insights. Here’s a practical, step-by-step guide to configuring these metrics in JIRA for optimal tracking and collaboration. With Typo’s integration, teams gain additional capabilities for managing, analyzing, and discussing metrics collaboratively.

Step 1: Configure Key Dashboards for Visibility

Setting up dashboards in JIRA for metrics like Cycle Time, Developer Workload, and Team Velocity allows for quick access to critical data.

How to set up:

  1. Go to the Dashboards section in JIRA, select Create Dashboard, and add specific gadgets such as Cumulative Flow Diagram for WIP and Velocity Chart for Team Velocity.
  2. Position each gadget for easy reference, giving your team a visual summary of project progress at a glance.

Step 2: Use Typo’s Sprint Analysis for Enhanced Sprint Visibility

Typo’s sprint analysis offers an in-depth view of your team’s progress throughout a sprint, enabling engineering managers and developers to better understand performance trends, spot blockers, and refine future planning. Typo integrates seamlessly with JIRA to provide real-time sprint insights, including data on team velocity, task distribution, and completion rates.

Key features of Typo’s sprint analysis:

Detailed sprint performance summaries: Typo automatically generates sprint performance summaries, giving teams a clear view of completed tasks, WIP, and uncompleted items.

Sprint progress tracking: Typo visualizes your team’s progress across each sprint phase, enabling managers to identify trends and respond to bottlenecks faster.

Velocity trend analysis: Track velocity over multiple sprints to understand performance patterns. Typo’s charts display average, maximum, and minimum velocities, helping teams make data-backed decisions for future sprint planning.

Step 3: Leverage Typo’s Customizable Reports for Deeper Analysis

Typo enables engineering teams to go beyond JIRA’s native reporting by offering customizable reports. These reports allow teams to focus on specific metrics that matter most to them, creating targeted views that support sprint retrospectives and help track ongoing improvements.

Key benefits of Typo reports:

Customized metrics views: Typo’s reporting feature allows you to tailor reports by sprint, team member, or task type, enabling you to create a focused analysis that meets team objectives.

Sprint performance comparison: Easily compare current sprint performance with past sprints to understand progress trends and potential areas for optimization.

Collaborative insights: Typo’s centralized platform allows team members to add comments and insights directly into reports, facilitating discussion and shared understanding of sprint outcomes.

Step 4: Track Team Velocity with Typo’s Velocity Trend Analysis

Typo’s Velocity Trend Analysis provides a comprehensive view of team capacity and productivity over multiple sprints, allowing managers to set realistic goals and adjust plans according to past performance data.

How to use:

  1. Access Typo’s Velocity Trend Analysis to view velocity averages and deviations over time, helping your team anticipate work capacity more accurately.
  2. Use Typo’s charts to visualize and discuss the effects of any changes made to workflows or team processes, allowing for data-backed sprint planning.
  3. Incorporate these insights into future sprint planning meetings to establish achievable targets and manage team workload effectively.

Step 5: Automate Alerts and Notifications for Key Metrics

Setting up automated alerts in JIRA and Typo helps teams stay on top of metrics without manual checking, ensuring that critical changes are visible in real-time.

How to set up:

  1. Use JIRA’s automation rules to create alerts for specific metrics. For example, set a notification if a task’s Cycle Time exceeds a predefined threshold, signaling potential delays.
  2. Enable notifications in Typo for sprint analysis updates, such as velocity changes or WIP limits being exceeded, to keep team members informed throughout the sprint.
  3. Automate report generation in Typo, allowing your team to receive regular updates on sprint performance without needing to pull data manually.

Step 6: Host Collaborative Retrospectives with Typo

Typo’s integration makes retrospectives more effective by offering a shared space for reviewing metrics and discussing improvement opportunities as a team.

How to use:

  1. Use Typo’s reports and sprint analysis as discussion points in retrospective meetings, focusing on completed vs. planned work, Cycle Time efficiency, and WIP trends.
  2. Encourage team members to add insights or suggestions directly into Typo, fostering collaborative improvement and shared accountability.
  3. Document key takeaways and actionable steps in Typo, ensuring continuous tracking and follow-through on improvement efforts in future sprints.

Read more: Moving beyond JIRA Sprint Reports 

Monitoring Scope Creep

Scope creep—when a project’s scope expands beyond its original objectives—can disrupt timelines, strain resources, and lead to project overruns. Monitoring scope creep is essential for agile teams that need to stay on track without sacrificing quality. 

In JIRA, tracking scope creep involves setting clear boundaries for task assignments, monitoring changes, and evaluating their impact on team workload and sprint goals.

How to Monitor Scope Creep in JIRA

  1. Define scope boundaries: Start by clearly defining the scope of each project, sprint, or epic in JIRA, detailing the specific tasks and goals that align with project objectives. Make sure these definitions are accessible to all team members.
  2. Use the issue history and custom fields: Track changes in task descriptions, deadlines, and priorities by utilizing JIRA’s issue history and custom fields. By setting up custom fields for scope-related tags or labels, teams can flag tasks or sub-tasks that deviate from the original project scope, making scope creep more visible.
  3. Monitor workload adjustments with Typo: When scope changes are approved, Typo’s integration with JIRA can help assess their impact on the team’s workload. Use Typo’s reporting to analyze new tasks added mid-sprint or shifts in priorities, ensuring the team remains balanced and prepared for adjusted goals.
  4. Sprint retrospectives for reflection: During sprint retrospectives, review any instances of scope creep and assess the reasons behind the adjustments. This allows the team to identify recurring patterns, evaluate the necessity of certain changes, and refine future project scoping processes.

By closely monitoring and managing scope creep, agile teams can keep their projects within boundaries, maintain productivity, and make adjustments only when they align with strategic objectives.

Building a Data-Driven Engineering Culture

Building a data-driven culture goes beyond tracking metrics; it’s about engaging the entire team in understanding and applying these insights to support shared goals. By fostering collaboration and using metrics as a foundation for continuous improvement, teams can align more effectively and adapt to challenges with agility.

Regularly revisiting and refining metrics ensures they stay relevant and actionable as team priorities evolve. To see how Typo can help you create a streamlined, data-driven approach, schedule a personalized demo today and unlock your team’s full potential.

How to Reduce Cyclomatic Complexity?

Think of reading a book with multiple plot twists and branching storylines. While engaging, it can also be confusing and overwhelming when there are too many paths to follow. Just as a complex storyline can confuse readers, high Cyclic Complexity can make code hard to understand, maintain, and test, leading to bugs and errors. 

In this blog, we will discuss why high cyclomatic complexity can be problematic and ways to reduce it.

What is Cyclomatic Complexity? 

Cyclomatic Complexity, a software metric, was developed by Thomas J. Mccabe in 1976. It is a metric that indicates the complexity of the program by counting its decision points. 

A higher cyclomatic Complexity score reflects more execution paths, leading to increased complexity. On the other hand, a low score signifies fewer paths and, hence, less complexity. 

Cyclomatic Complexity is calculated using a control flow graph: 

M = E - N + 2P

M = Cyclomatic Complexity

N = Nodes (Block of code) 

E = Edges (Flow of control)

P = Number of Connected Components 

Why is High Cyclomatic Complexity Problematic? 

Increases Error Prone 

The more complex the code is, the more the chances of bugs. When there are many possible paths and conditions, developers may overlook certain conditions or edge cases during testing. This leads to defects in the software and becomes challenging to test all of them. 

Leads to Cognitive Complexity 

Cognitive complexity refers to the level of difficulty in understanding a piece of code. 

Cyclomatic Complexity is one of the factors that increases cognitive complexity. Since, it becomes overwhelming to process information effectively for developers, which makes it harder to understand the overall logic of code.

Difficulty in Onboarding 

Codebases with high cyclomatic Complexity make onboarding difficult for new developers or team members. The learning curve becomes steeper for them and they require more time and effort to understand and become productive. This also leads to misunderstanding and they may misinterpret the logic or overlook critical paths. 

Higher Risks of Defects

More complex code leads to more misunderstandings, which further results in higher defects in the codebase. Complex code is more prone to errors as it hinders adherence to coding standards and best practices. 

Rise in Maintainance Efforts 

Due to the complex codebase, the software development team may struggle to grasp the full impact of their changes which results in new errors. This further slows down the process. It also results in ripple effects i.e. difficulty in isolating changes as one modification can impact multiple areas of application. 

How to Reduce Cyclomatic Complexity? 

Function Decomposition

  • Single Responsibility Principle (SRP): This principle states that each module or function should have a defined responsibility and one reason to change. If a function is responsible for multiple tasks, it can result in bloated and hard-to-maintain code. 
  • Modularity: This means dividing large, complex functions into smaller, modular units so that each piece serves a focused purpose. It makes individual functions easier to understand, test, and modify without affecting other parts of the code.
  • Cohesion: Cohesion focuses on keeping related code close to functions and modules. When related functions are grouped together, it results in high cohesion which helps with readability and maintainability.
  • Coupling: This principle states to avoid excessive dependencies between modules. This will reduce the complexity and make each module more self-contained, enabling changes without affecting other parts of the system.

Conditional Logic Simplification

  • Guard Clauses: Developers must implement guard clauses to exit from a function as soon as a condition is met. This avoids deep nesting and enhances the readability and simplicity of the main logic of the function. 
  • Boolean Expressions: Use De Morgan's laws and simplify Boolean expressions to reduce the complexity of conditions. For example, rewriting! (A && B) as ! A || !B can sometimes make the code easier to understand.
  • Conditional Expressions: Consider using ternary operators or switch statements where appropriate. This will condense complex conditional branches into more concise expressions which further enhance their readability and reduce code size.
  • Flag Variables: Avoid unnecessary flag variables that track control flow. Developers should restructure the logic to eliminate these flags which can lead to simpler and cleaner code.

Loop Optimization

  • Loop Unrolling: Expand the loop body to perform multiple operations in each iteration. This is useful for loops with a small number of iterations as it reduces loop overhead and improves performance.
  • Loop Fusion: When two loops iterate over the same data, you may be able to combine them into a single loop. This enhances performance by reducing the number of loop iterations and boosting data locality.
  • Loop Strength Reduction: Consider replacing costly operations in loops with less expensive ones, such as using addition instead of multiplication where possible. This will reduce the computational cost within the loop.
  • Loop Invariant Code Motion: Prevent redundant computation by moving calculations that do not change with each loop iteration outside of the loop. 

Code Refactoring

  • Extract Method: Move repetitive or complex code segments into separate functions. This simplifies the original function, reduces complexity, and makes code easier to reuse.
  • Introduce Explanatory Variables: Use intermediate variables to hold the results of complex expressions. This can make code more readable and allow others to understand its purpose without deciphering complex operations.
  • Replace Magic Numbers with Named Constants: Magic numbers are hard-coded numbers in code. Instead of directly using them, create symbolic constants for hard-coded values. It makes it easy to change the value at a later stage and improves the readability and maintainability of the code.
  • Simplify Complex Expressions: Break down long, complex expressions into smaller, more digestible parts to improve readability and reduce cognitive load on the reader.

5. Design Patterns

  • Strategy Pattern: This pattern allows developers to encapsulate algorithms within separate classes. By delegating responsibilities to these classes, you can avoid complex conditional statements and reduce overall code complexity.
  • State Pattern: When an object has multiple states, the State Pattern can represent each state as a separate class. This simplifies conditional code related to state transitions.
  • Observer Pattern: The Observer Pattern helps decouple components by allowing objects to communicate without direct dependencies. This reduces complexity by minimizing the interconnectedness of code components.

6. Code Analysis Tools

  • Static Code Analyzers: Static Code Analysis Tools like Typo or Sonarqube, can automatically highlight areas of high complexity, unused code, or potential errors. This allows developers to identify and address complex code areas proactively.
  • Code Coverage Tools: Code coverage is a measure that indicates the percentage of a codebase that is tested by automated tests. Tools like Typo measures code coverage, highlighting untested areas. It helps ensure that the tests cover a significant portion of the code which helps identifies untested parts and potential bugs.

Other Ways to Reduce Cyclomatic Complexity 

  • Identify and remove dead code to simplify the codebase and reduce maintenance efforts. This keeps the code clean, improves performance, and reduces potential confusion.
  • Consolidate duplicate code into reusable functions to reduce redundancy and improve consistency. This makes it easier to update logic in one place and avoid potential bugs from inconsistent changes.
  • Continuously improve code structure by refactoring regularly to enhance readability, and maintainability, and reduce technical debt. This ensures that the codebase evolves to stay efficient and adaptable to future needs.
  • Perform peer reviews to catch issues early, promote coding best practices, and maintain high code quality. Code reviews encourage knowledge sharing and help align the team on coding standards.
  • Write Comprehensive Unit Tests to ensure code functions correctly and supports easier refactoring in the future. They provide a safety net which makes it easier to identify issues when changes are made.

Typo - An Automated Code Review Tool

Typo’s automated code review tool identifies issues in your code and auto-fixes them before you merge to master. This means less time reviewing and more time for important tasks. It keeps your code error-free, making the whole process faster and smoother.

Key Features:

  • Supports top 8 languages including C++ and C#.
  • Understands the context of the code and fixes issues accurately.
  • Optimizes code efficiently.
  • Provides automated debugging with detailed explanations.
  • Standardizes code and reduces the risk of a security breach

 

Conclusion 

The cyclomatic complexity metric is critical in software engineering. Reducing cyclomatic complexity increases the code maintainability, readability, and simplicity. By implementing the above-mentioned strategies, software engineering teams can reduce complexity and create a more streamlined codebase. Tools like Typo’s automated code review also help in identifying complexity issues early and providing quick fixes. Hence, enhancing overall code quality.

Beyond Burndown Chart: Tracking Engineering Progress

Burndown charts are essential instruments for tracking the progress of agile teams. They are simple and effective ways to determine whether the team is on track or falling behind. However, there may be times when a burndown chart is not ideal for teams, as it may not capture a holistic view of the agile team’s progress. 

In this blog, we have discussed the latter part in greater detail. 

What is a Burndown Chart? 

Burndown Chart is a visual representation of the team’s progress used for agile project management. They are useful for scrum teams and agile project managers to assess whether the project is on track or not. 

The primary objective is to accurately depict the time allocations and plan for future resources. 

Components of Burndown Chart

Axes

There are two axes: x and y. The horizontal axis represents the time or iteration and the vertical axis displays user story points. 

Ideal Work Remaining 

It represents the remaining work that an agile team has at a specific point of the project or sprint under an ideal condition. 

Actual Work Remaining 

It is a realistic indication of a team's progress that is updated in real time. When this line is consistently below the ideal line, it indicates the team is ahead of schedule. When the line is above, it means they are falling behind. 

Project/Sprint End

It indicates whether the team has completed a project/sprint on time, behind or ahead of schedule. 

Data Points

The data points on the actual work remaining line represents the amount of work left at specific intervals i.e. daily updates. 

Types of Burndown Chart 

There are two types of Burndown Chart: 

Product Burndown Chart 

This type of burndown chart focuses on the big picture and visualises the entire project. It helps project managers and teams monitor the completion of work across multiple sprints and iteration. 

Sprint Burndown Chart 

Sprint Burndown chart particularly tracks the remaining work within a sprint. It indicates progress towards completing the sprint backlog. 

Advantages of Burndown Chart 

Visualises Progress 

Burndown Chart captures how much work is completed and how much is left. It allows the agile team to compare the actual progress with the ideal progress line to track if they are ahead or behind the schedule. 

Encourages Teams 

Burndown Chart motivates teams to align their progress with the ideal line. These small milestones boost morale and keep their motivation high throughout the sprint. It also reinforces the sense of achievement when they see their tasks completed on time. 

Informs Retrospectives 

It helps in analyzing performance over sprint during retrospection. Agile teams can review past data through burndown Charts to identify patterns, adjust future estimates, and refine processes for improved efficiency. It allows them to pinpoint periods where progress went down and help to uncover blockers that need to be addressed. 

Shows a Direct Comparison 

Burndown Chart visualizes the direct comparison of planned work and actual progress. It can quickly assess whether a team is on track to meet the goals, and monitor trends or recurring issues such as over-committing or underestimating tasks. 

Burndown Chart can be Misleading too. Here’s Why? 

While the Burndown Chart comes with lots of pros, it could be misleading as well. It focuses solely on the task alone without accounting for individual developer productivity. It ignores the aspects of agile software development such as code quality, team collaboration, and problem-solving. 

Burndown Chart doesn’t explain how the task impacted the developer productivity or the fluctuations due to various factors such as team morale, external dependencies, or unexpected challenges. It also doesn’t focus on work quality which results in unaddressed underlying issues.

Other Limitations of Burndown Chart 

Oversimplification of Complex Projects 

While the Burndown Chart is a visual representation of Agile teams’ progress, it fails to capture the intricate layers and interdependencies within the project. It overlooks the critical factors that influence project outcomes which may lead to misinformed decisions and unrealistic expectations. 

Ignores Scope Changes 

Scope Creep refers to modification in the project requirement such as adding new features or altering existing tasks. Burndown Chart doesn’t take note of the same rather shows a flat line or even a decline in progress which can signify that the team is underperforming, however, that’s not the actual case. This leads to misinterpretation of the team’s progress and overall project health. 

Gives Equal Weight to all the Tasks

Burndown Chart doesn’t differentiate between easy and difficult tasks. It considers all of the tasks equal, regardless of their size, complexity, or effort required. Whether the task is on priority or less impactful, it treats every task as the same. Hence, obscuring insights into what truly matters for the project's success. 

Neglects Team Dynamics 

Burndown Chart treats team members equally. It doesn't take individual contributions into consideration as well as other factors including personal challenges. It also neglects how well they are working with each other, sharing knowledge, or supporting each other in completing tasks. 

What are the Alternatives to Burndown Chart? 

Gantt Charts

Gantt Charts are ideal for complex projects. They are a visual representation of a project schedule using horizontal axes. They provide a clear timeline for each task i.e. when the project starts and ends as well as understanding overlapping tasks and dependencies between them. 

Cumulative Flow Diagram 

CFD visualizes how work moves through different stages. It offers insight into workflow status and identity trends and bottlenecks. It also helps in measuring key metrics such as cycle time and throughput. 

Kanban Boards 

Kanban Boards is an agile management tool that is best for ongoing work. It helps to visualize work, limit work in progress, and manage workflows. They can easily accommodate changes in project scope without the need for adjusting timelines. 

Burnup Chart 

Burnup Chart is a quick, easy way to plot work schedules on two lines along a vertical axis. It shows how much work has been done and the total scope of the project, hence, providing a clearer picture of project completion. 

Developer Intelligence Platforms 

DI platforms focus on how smooth and satisfying a developer experience is. It streamlines the development process and offers a holistic view of team productivity, code quality, and developer satisfaction. These platforms also provide real-time insights into various metrics that reflect the team’s overall health and efficiency beyond task completion alone.

Typo - An Effective Sprint Analysis Tool

One such platform is Typo, which goes beyond the traditional metrics. Its sprint analysis is an essential tool for any team using an agile development methodology. It allows agile teams to monitor and assess progress across the sprint timeline, providing visual insights into completed work, ongoing tasks, and remaining time. This visual representation allows to spot potential issues early and make timely adjustments.

Our sprint analysis feature leverages data from Git and issue management tools to focus on team workflows. They can track task durations, identify frequent blockers, and pinpoint bottlenecks.

With easy integration into existing Git and Jira/Linear/Clickup workflows, Typo offers:

  • Velocity Chart that shows completed work in past sprints
  • Sprint Backlog that displays all tasks slated for completion within the sprint
  • Tracks the status of each sprint issue.
  • Measures task durations
  • Highlights areas where work is delayed and identifies task blocks and causes. 
  • Historical Data Analysis that compares sprint performance over time.

Hence, helping agile teams stay on track, optimize processes, and deliver quality results efficiently.

Conclusion 

While the burndown chart is a valuable tool for visualizing task completion and tracking progress, it often overlooks critical aspects like team morale, collaboration, code quality, and factors impacting developer productivity. There are several alternatives to the burndown chart, with Typo’s sprint analysis tool standing out as a powerful option. Through this, agile teams gain a more comprehensive view of progress, fostering resilience, motivation, and peak performance.

Understanding the Human Side of DevOps: Aligning Goals Across Teams

One of the biggest hurdles in a DevOps transformation is not the technical implementation of tools but aligning the human side—culture, collaboration, and incentives. As a leader, it’s essential to recognize that different, sometimes conflicting, objectives drive both Software Engineering and Operations teams.

Engineering often views success as delivering features quickly, whereas Operations focuses on minimizing downtime and maintaining stability. These differing incentives naturally create friction, resulting in delayed deployment cycles, subpar product quality, and even a toxic work environment.

The key to solving this? Cross-functional team alignment.

Before implementing DORA metrics, you need to ensure both teams share a unified vision: delivering high-quality software at speed, with a shared understanding of responsibility. This requires fostering an environment of continuous communication and trust, where both teams collaborate to achieve overarching business goals, not just individual metrics.

Why DORA Metrics Outshine Traditional Metrics

Traditional performance metrics, often focused on specific teams (like uptime for Operations or feature count for Engineering), incentivize siloed thinking and can lead to metric manipulation. Operations might delay deployments to maintain uptime, while Engineering rushes features without considering quality.

DORA metrics, however, provide a balanced framework that encourages cooperative success. For example, by focusing on Change Failure Rate and Deployment Frequency, you create a feedback loop where neither team can game the system. High deployment frequency is only valuable if it’s accompanied by low failure rates, ensuring that the product's quality improves alongside speed.

In contrast to traditional metrics, DORA's approach emphasizes continuous improvement across the entire delivery pipeline, leading to better collaboration between teams and improved outcomes for the business. The holistic nature of these metrics also forces leaders to look at the entire value stream, making it easier to identify bottlenecks or systemic issues early on.

Leveraging DORA Metrics for Long-Term Innovation

While the initial focus during your DevOps transformation should be on Deployment Frequency and Change Failure Rate, it’s important to recognize the long-term benefits of adding Lead Time for Changes and Time to Restore Service to your evaluation. Once your teams have achieved a healthy rhythm of frequent, reliable deployments, you can start optimizing for faster recovery and shorter change times.

A mature DevOps organization that excels in these areas positions itself to innovate rapidly. By decreasing lead times and recovery times, your team can respond faster to market changes, giving you a competitive edge in industries that demand agility. Over time, these metrics will also reduce technical debt, enabling faster, more reliable development cycles and an enhanced customer experience.

Building a Culture of Accountability with Metrics Pairing

One overlooked aspect of DORA metrics is their ability to promote accountability across teams. By pairing Deployment Frequency with Change Failure Rate, for example, you prevent one team from achieving its goals at the expense of the other. Similarly, pairing Lead Time for Changes with Time to Restore Service encourages teams to both move quickly and fix issues effectively when things go wrong.

This pairing strategy fosters a culture of accountability, where each team is responsible not just for hitting its own goals but also for contributing to the success of the entire delivery pipeline. This mindset shift is crucial for the success of any DevOps transformation. It encourages teams to think beyond their silos and work together toward shared outcomes, resulting in better software and a more collaborative work environment.

Early Wins and Psychological Momentum: The Power of Small Gains

DevOps transformations can be daunting, especially for teams that are already overwhelmed by high workloads and a fast-paced development environment. One strategic benefit of starting with just two metrics—Deployment Frequency and Change Failure Rate—is the opportunity to achieve quick wins.

Quick wins, such as reducing deployment time or lowering failure rates, have a significant psychological impact on teams. By showing progress early in the transformation, you can generate excitement and buy-in across the organization. These wins build momentum, making teams more eager to tackle the larger, more complex challenges that lie ahead in the DevOps journey.

As these small victories accumulate, the organizational culture shifts toward one of continuous improvement, where teams feel empowered to take ownership of their roles in the transformation. This incremental approach reduces resistance to change and ensures that even larger-scale initiatives, such as optimizing Lead Time for Changes and Time to Restore Service, feel achievable and less stressful for teams.

The Role of Leadership in DevOps Success

Leadership plays a critical role in ensuring that DORA metrics are not just implemented but fully integrated into the company’s DevOps practices. To achieve true transformation, leaders must:

  • Set the right expectations: Make it clear that the goal of using DORA metrics is not just to “move the needle” but to deliver better software faster. Explain how the metrics contribute to business outcomes.
  • Foster a culture of psychological safety: Encourage teams to see failures as learning opportunities. This cultural shift helps improve the Change Failure Rate without resorting to blame or fear.
  • Lead by example: Show that leadership is equally committed to the DevOps transformation by adopting new tools, improving communication, and advocating for cross-functional collaboration.
  • Provide the right tools and resources: For DORA metrics to be effective, teams need the right tools to measure and act on them. Leaders must ensure their teams have access to automated pipelines, robust monitoring tools, and the support needed to interpret and respond to the data.

Typo: Accelerating Your DevOps Transformation with Streamlined Documentation

In your DevOps journey, the right tools can make all the difference. One often overlooked aspect of DevOps success is the need for effective, transparent documentation that evolves as your systems change. Typo, a dynamic documentation tool, plays a critical role in supporting your transformation by ensuring that everyone—from engineers to operations teams—can easily access, update, and collaborate on essential documents.

Typo helps you:

  • Maintain up-to-date documentation that adapts with every deployment, ensuring that your team never has to work with outdated information.
  • Reduce confusion during deployments by providing clear, accessible, and centralized documentation for processes and changes.
  • Improve collaboration between teams, as Typo makes it easy to contribute and maintain critical project information, supporting transparency and alignment across your DevOps efforts.

With Typo, you streamline not only the technical but also the operational aspects of your DevOps transformation, making it easier to implement and act on DORA metrics while fostering a culture of shared responsibility.

Conclusion: Starting Small, Thinking Big

Starting a DevOps transformation can feel overwhelming, but with the focus on DORA metrics—especially Deployment Frequency and Change Failure Rate—you can begin making meaningful improvements right away. Your organization can smoothly transition into a high-performing, innovative powerhouse by fostering a collaborative culture, aligning team goals, and leveraging tools like Typo for documentation.

The key is starting with what matters most: getting your teams aligned on quality and speed, measuring the right things, and celebrating the small wins along the way. From there, your DevOps transformation will gain the momentum needed to drive long-term success.

Webinar: ‘The Hows and Whats of DORA' with Dave Farley and Denis Čahuk

In this DORA exclusive webinar, hosted by Kovid from Typo, notable software engineers Dave Farley and Denis Čahuk discuss the profound impact of DORA metrics on engineering productivity.

Dave, co-author of 'Continuous Delivery,' emphasized the transition to continuous delivery (CD) and its significant benefits, involving systematic quality improvements and efficient software release cycles. Denis, a technical coach and TDD/DDD expert, shared insights into overcoming resistance to CD adoption. The discussion covered the challenges associated with measuring productivity, differentiating between continuous delivery and continuous deployment, and the essential role of team dynamics in successful implementation. The session also addressed audience questions about balancing speed and quality, using DORA metrics effectively, and handling burnout and engineering well-being.

Timestamps

  • 00:00 - Introduction
  • 00:14 - Meet the Experts: Dave Farley and Denis Čahuk
  • 01:01 - Dave Farley's Journey and Hobbies
  • 02:38 - Denis Čahuk's Passion for Problem Solving
  • 06:37 - Challenges in Adopting Continuous Delivery
  • 11:34 - Engineering Mindset and Continuous Improvement
  • 14:54 - Measuring Success with DORA Metrics
  • 25:38 - Addressing Burnout and Team Performance
  • 32:33 - The Benefits of Ensemble Programming
  • 33:34 - ThoughtWorks and Lean Development
  • 34:45 - Social Variants in Agile Practices
  • 36:52 - Continuous Delivery and Team Well-being
  • 42:59 - The Importance of TDD and Pairing
  • 46:45 - Q&A Session
  • 01:00:09 - Conclusion and Final Thoughts

Links and Mentions

Transcript

Kovid Batra: All right. So time to get started. Uh, thanks for joining in for this DORA exclusive webinar, The Hows and Whats of DORA session three, powered by Typo. I am Kovid, founding member at Typo and your host for today's webinar. With me today, I have two extremely passionate software engineers. Please welcome the DORA expert tonight, Dave Farley. Dave is a co-author of award-winning books, Continuous Delivery, Modern Software Engineering, and a pioneer in DevOps. Along with him, we have the technical coach, Denis Čahuk, who is TDD, DDD expert, and he is a stress-free high-performance development culture provider in the tech teams. Welcome to the show, both of you. Thank you so much for joining in.

Dave Farley: Pleasure. Thank you for having me.

Denis Čahuk: Thank you for having me.

Kovid Batra: Great guys. So I think we will take it one by one. Uh, so let's, let's, let's start with, uh, I think, uh, Dave first. Uh, so Dave, uh, this is a ritual that we follow on this webinar. You have to tell us about yourself, uh, that your LinkedIn profile doesn't tell. So you have to give us a quick, sweet intro about yourself.

Dave Farley: Okay. Um, I'm a long-time software developer who really enjoys problem-solving. I really enjoy that aspect of the job. I, if you want, if you want to get me, get me to come and work at your place, you tell me that the problem's hard to solve. And that's, that's the kind of stuff that I like, and I've spent much of my career doing some of those hard to solve problems and figuring out ways in which to make that easier.

Kovid Batra: Great. All right. So I think, Dave, uh, apart from that, uh, anything that you love beyond software engineering that you enjoy doing?

Dave Farley: Yeah, my wife says that my hobby is collecting hobbies. So, so I'm, I'm a guitarist. I used to, I used to play in rock bands years ago. Um, I, until fairly recently, I was a member of the British aerobatics team, flying competition aerobatics in a 300 horsepower, plus 10, minus 10 G, uh, aerobatic airplane, which, which was awesome, but, uh, I don't do that anymore. I've stopped very recently.

Kovid Batra: That's amazing, man. That's really amazing. Great. Thank you. Thank you so much for that, uh, intro about yourself and, uh, Denis over to you, man.

Denis Čahuk: Um, like Dave, I really like problem solving, but, but I like involving, uh, I spent the beginning of my career in focusing too much on the compiler and I like focusing on the human problems as well. So how, what, what makes the team tick and in particular with TDD, it really, really scratched an itch about what makes teams resistant and what makes teams a little bit more open to change and improvement and dialogue, especially dialogue. Uh, that has become my specialty since. So yes, I brand myself as a TDD, DDD coach, but that's primarily there to drive engagement. I'm, I'm super interested in engineering leadership and specifically what drives trends and what helps people, what helps, uh, engineers, engineering teams overcome their own resistance, sort of, if they're in their own way, you know, why is that there, how to, how to resolve any kind of, um, blockers, let's say, human blockers, not, not, not the compiler kind, uh, in engineering things. I don't plan any planes, but I do have, I do share, uh, Dave's passion for music. So I do have a guitar and, uh, the drum there behind me. So whenever I'm not streaming or coding, I am jamming out as much as I can.

Kovid Batra: Perfect. Perfect, man. All right. So I think it's time we get started and move to the, to move to the main section. Uh, so the first thing that I love to talk to you, uh, Dave first, uh, so you have this, uh, YouTube channel, uh, and it's not in your name, right? It's, it's Continuous Delivery. Uh, what, what makes Continuous Delivery so important to you?

Dave Farley: Somebody else said to, this to me very recently, which, which I agree with, which is that I think that Continuous Delivery, without seeming too immodest, because my name's associated with it, but I think it represents a step change in what we can do as software developers. I think it's a significant step forward in our ability to create better software faster. If you embrace the ideas of continuous delivery, which includes things like test-driven development, in DDD, as Denis was describing, and is very team-centered as well, which Denis was also talking about. If you, if you embrace those ideas and adopt the disciplines of continuous delivery, which fundamentally, all devolve into one idea, which is working software is always in a releasable state, then you get quite dramatically better outcomes. And I think without too much fear of contradiction, continuous delivery represents the state of the art in software development. It's what the best organizations at software development do. And so, I think it's an important idea and it's as I said, although I sound rather immodest because I'm one of the people that helped at least put the language to it, but people were doing these things, but Jez, Jez and my book define the language around which continuous delivery talking is usually structured these days. Um, and so, so I think it's an important idea and I think that software engineering is one of the most important things that we do in our society and it matters a lot and we ought to be better at it as an industry and I think that this is how we get better at it. So, so I get an awful lot of job satisfaction and personal pleasure on trying to help people on their journey towards achieving continuous delivery.

Kovid Batra: And I think you're being just modest here. Your book just didn't define or give a language there. It did way, way more than that. And, uh, kudos to you for that. Uh, I think my next question would be like, what's that main ingredient, uh, that separates a team following CD and a team not following CD? What do you think makes the big difference there?

Dave Farley: There are some easy answers. Let me just tackle the difficult answer first, because I think the difficulty with continuous delivery is that the idea is simple, but it's so challenging to most people that it's very difficult to adopt. It challenges the way in which we think about software. I think it challenges to some degree. I'm a bit of a pop psychologist. I think in many ways it challenges, um, our very understanding of what software is to some extent, and certainly what software development is. And that's difficult. That means that it changes every person's role in undertaking this. It, as I said already, it's a much more team centered approach, I think, uh, to be able to achieve this permanent releasability of our software. But fundamentally, I think if you want to boil it down to more straightforward concepts to think about, I think that what we're talking about here is kind of applying what I think of as a kind of scientific rationalism to solving problems in software. And so the biggest part of that, the two biggest ideas there, from my point of view, are working in small steps and essentially, treating each of those steps as a little experiment and assuming that we're going to be wrong. So it's always one of the big ideas in science is that you start off assuming that your ideas are wrong, and then you try and figure out how and why they're wrong. I think we do the same thing in continuous delivery and software engineering, modern software engineering. We try to figure out how can we detect where our ideas are wrong, and then we try and detect where they're wrong, in those places and find out if they're wrong or not and then correct them. And that's how we build a better software. And so this, I think that goes quite deep and it affects quite a lot about how we undertake our work. But I think that one of the step changes in capability is that I think that previous thinking about software development kind of started off from the assumption that our job is to get everything perfectly right from the start. And that's simply irrational and impossible. And so, instead of taking a more scientific mindset and starting off assuming that we will be wrong, and so we give ourselves the freedom to be wrong and the ability to um, recover from it easily is almost the whole game.

Kovid Batra: Got it. I think Denis has a question. He wants to, yeah, please go ahead.

Denis Čahuk: Sure. I'm going to go off script. I think I like that distinction of psychologist. Sometimes I feel myself, find myself in a similar role. And I think the core disagreement comes from this idea of a lot of engineers, organizational owners, CTOs don't like this idea that their code is an experiment. They want some like certain assurances that it has been inspected and that it's, it's not, it's not something that we expect to fail. So from their perspective, non-CD adopters think that the scientific rationale is hard inspection towards requirements rather than conducting an experiment. And I see that, um, sort of providing a lot of resistance regarding CD adoption cause it is very hard to do, or it's very hard to come from that rationale and say, okay, we're now doing CD, but we're not doing CD right now. We're adopting CD right now. So we're kind of doing it, but not doing it. And it just creates a lot of tension and resistance in companies. Did you find similar situations? How do you, how do you sort of massage this sort of identity shift identity crisis?

Dave Farley: Yeah. Yeah I think, I think absolutely that's a thing and, and that is the challenge. It is that is to try and find ways to help those people to see the light. So I know I sound like an evangelist. Yeah, but, but I guess I see that as part of my role. But..

Denis Čahuk: You did write the book, so..

Dave Farley: Yeah, so, so, so I think this is in everybody's interest. I mean, the data backs me up. The DORA data says that if you adopt the practices of continuous delivery, you spend 44 percent as an organization more time on building new features than if you don't. That's pretty slam dunk in terms of value as far as I'm concerned, and there's lots more to it than that. But, you know, so why wouldn't anybody want to be able to build better software faster? And this is the best way that we know of so far, how to do that. So, so that seems like a reasonably rational way of deciding that this is a good idea, but that's not enough to change people's minds. And you've got to change people's minds in all sorts of different ways. Um, I think it's important to make these sorts of things, but going back to those people that you said that, you know, engineers who think it's their job to get it right first time, they don't understand what engineering is. Managers who want to build the software more quickly, get more features out. They don't understand what building software more quickly really means because if either of those groups knew those things, they'd be shouting out and demanding continuous delivery, because it's the thing that you need. We don't know the right answers first time. Look at any technology. Let alone any product and its history. Look at the aeroplane. In the first aeroplane that could carry a person under power in a controllable way was the Wright Flyer in 1903. And for the first 20 or 30 years, all aeroplanes were death traps. People were, they were such dangerous devices. But engineering as a discipline adopted an incremental approach to learning and discovery to improve the airplane. And by 2017, two thirds of the planet, the equivalent of two thirds of the population of the planet, flew in commercial airliners and nobody was killed. That's what engineering does. It's an incremental process. It doesn't, we don't, we never ever, ever get it right first time. The iPhone, the first iPhone didn't have an app store, didn't have a camera, didn't have Siri, didn't have none of these things, didn't..

Denis Čahuk: Multitasking.

Dave Farley: Didn't have multitasking, all of these things. And now we have these amazing devices in our pockets that can do all sorts of amazing things that the original designers of the iPhone didn't actually predict. I'm sure that they had vague wishes in their minds, but they didn't predict them ahead of time. That's not how engineering works. So the way that engineering works is by exploration and discovery. And we need to, to be good at it, we need to organize to be good at exploration and discovery. And the way that, you know, so if we want to build things more efficiently, then we would, we need to adopt the disciplines that allow us to make these mistakes and accept that we will and look, you know, detect them as quickly as we can and learn from them as quickly as we can. And that's, you know, that's why, to my mind, you know, the results of the DORA thing, so there's no trade-off between speed and quality because you work in these small steps, you get faster feedback on, on whether your ideas are good or bad. So those small steps are important. And then when you find out that they're a bad idea, you correct them. And that's how you get to good.

Kovid Batra: Totally. I think, uh, one very good point, uh, here, we are sure like now CD and other practices like TDD impact engineering in a very positive way, improving the overall productivity and actually delivering value and the slam dunk like 44 percent more value delivered, right? But when it really comes to proving that number to these teams, uh, do you, like, do you use any framework? Do you use like DORA or SPACE to tell whether implementing CD was effective in a way? How do you measure that impact?

Dave Farley: No, most, mostly I recommend that people use the DORA metrics. Um, I, let me just talk momentarily about that because I think that that's important. I think the work of Nicole and the rest of the team in starting off the DORA was close to genius in identifying, as far as I can think of, the only generic measures in software. If you think about what, what the, the DORA metrics of stability and throughput measure, um, it's, um, the quality of the software that we produce and the rate at which we can produce software of that quality. That stability is the quality. Throughput is the efficiency with which we can produce software of that quality. Those are fundamental. They say nothing at all about the nature of the problem we're solving, the technology we're using, or anything else. If you're writing, if you're configuring SAP to do a better job of whatever it is that you're trying to do, that's still a good measure of success, stability and throughput. Um, if I'm writing some low-level code for an operating system, that's still a good measure of success. It doesn't matter. So, so we have these generic measures. Now they aren't enough to measure everything that's important in software. What they do is that they tell us whether we're building software right. They don't tell us whether we're building the right software, for example. So we need different kinds of experiments to understand other aspects of software. But I don't think there's much else. There's nothing else that I can think of that's in the same category. Stability and throughput in terms of the generosity of those measurements. And so, if you want a place to start of what to measure, start with stability and throughput and then figure out how to measure the other things out because they're going to be dependent on your context.

I'm a big fan of Site Reliability Engineering as a model for this. It talks in terms of, um, um, SLOs and SLIs, Service Level Indicators and Service Level Objectives. So the Service Level Indicator is what measure will determine the success of this service. So you identify, for every single feature, you identify what you should measure to know whether it's good or not. And then you set an objective of what score on that scale you want to achieve for this thing. That's a good way of measuring things, but it's kinda difficult. The huge difference is it's completely contextual, not even application by application, but feature by feature. So one feature might improve the latency, another feature might improve the rate at which we recruit new customers. And we've got to figure out, you know, that's how we get experimental with those kinds of things, by being more specific about and targeted with what we measure. I am skeptical of most of the generic measures. Not because I don't want them, it's just that I don't think that most of the others are generic and do what we want them to. Um, I'm not quite sure what I make of the SPACE framework, which is Nicole's new work on measuring developer, developer productivity. She's very smart and very good at the research-driven stuff. Uh, I spoke to her about some of this stuff on my, my podcast and, um, she had interesting things to say about it. I am still nervous of measuring individual developer productivity because as Denis said in his introduction, one of the really important things is how well a team works. So I think modern software development. unless it's building something trivial usually, is a team game. It's a matter of people coming together and organizing themselves in a way to be able to achieve some goal. And that takes an awful lot, and you can have people working with different levels of skill, experience, diligence, who may be still contributing strongly to the team, even if they're not pulling their weight in other respects. So I think it's a complicated thing to measure, a very human thing to measure. So, so I'm a bit suspect of that, but I'm fairly confident that Nicole will have some data that proves me wrong. But I, you know, that's, that's my position so far.

Kovid Batra: Totally makes sense. I think with almost all the frameworks, there have been some level of challenges and so is with DORA, SPACE, but I think in your experience, when, when you have seen, uh, and you have helped teams implement such practices, uh, what do you think have become the major reasons where they get stuck, not implementing these frameworks, not implementing proper engineering metrics? What, what, what stops them from doing it? What stops them from adopting it?

Dave Farley: I think specifically with using DORA, um, there are some complexities. If you, if you, if you are in a, a regular kind of organization that hasn't been working in the ways in which we've been talking about so far, um, then measuring stuff, just, just measuring stuff is hard. You're not used to doing it. The number of organizations that I talked to that couldn't even tell you how much, excuse me, time was spent on a feature, they don't measure it. They don't know. And so just getting the basics in, the thinking in, to be able to start to be a little bit more quantitative on these things is hard. And that's hard for people like us probably to get our heads around a little bit because when you've got a working deployment pipeline, this stuff is actually pretty easy because you just instrument your deployment pipeline and it gives you all the answers pretty much. So I think that there's that kind of practical difficulty, but I don't think that's the big ticket problem. The big ticket problem is just the mindset, my, I am old enough and comfortable enough in my shoes to recognize that I'm a grumpy old man. Um, and part of my grumpy old manness is to look at our industry and think that our industry is largely a fashion industry. It's not a technical industry. And there's an awful lot of mythology that goes on in the software industry. That's simply nothing to do with doing a good job. It's just what everybody thinks everybody else is doing. And I think that's incredibly common. And you've got to overcome that because if you're talking to a team, I'm going to trample on some people's sacred cow right now, but if you're talking to a team that works with feature branching, the evidence is in. Feature branching doesn't work as well as trunk-based development. That's more learning that we got from the DORA metrics, measuring those. Teams that work with feature branches build slightly lower quality code and they do it slightly more slowly than teams working on trunk. Now the problem is, is that it's almost inconceivable how you can do trunk-based development safely to people that buy into the, what I would think of as the mythology of feature branching. The fact that it, it, you can do it safely and you can do it safely at scale with complicated software, they start to deny because they assume that, that, that you can't, because they can't think of how you would do it. And that's the kind of difficulty that, that you face. It's not that it's a rational way of thinking about it, because I, I think it's very easy to defend why trunk-based development and continuous integration are more true, more, more, more accurate. You know, you, you organize things so that there's one point of truth. And in feature branching, you don't have one point of truth, you have multiple points of truth. And so it's clear that it's easier to determine whether the one point of truth is correct than deciding that multiple points of truth, that you don't know how you're going to integrate them together yet, is correct. You can't tell.

So it's, it's, it's tricky. So I think that there are rational ways of thinking that help us to do this, which is why I started, I've started to think about and talk about what we do as engineering more than as craft or just software development. If we do it well, uh, it's engineering and if we do it well and use engineering, we get a better result, which is kind of the definition of what engineering is in another discipline. If we work in certain ways, we do get better results. I think that's important stuff. So it's very, very hard to convince people and to get them away from their, what I would think of as mythologies sometimes. Um, and it's also difficult to be able to have these kinds of conversations and not seem very dogmatic. I get accused of being dogmatic about this stuff all of the time. Being arrogant for a moment. I think there's a big difference between being dogmatic and being right. I, I think that if we talk about, you know, having evidence like the DORA metrics, having a model like the way that I describe how these things stitch together and the reasons why they work and just having a favorite way of doing things, there's a difference between those things. I don't like continuous integration because it's my favorite. I like continuous integration because it works better than anything else. I like TDD not because I think it's my ideal for designing software. It's just that it's a better way of designing software than anything else. That's my belief. And, and so it's difficult to have these kinds of conversations because inevitably, you know, my viewpoints are going to be covered, colored by my experiences and what I've seen. But I try hard to be honest myself as an aspiring engineer and scientific rationalist. I try to be true to myself and try to critique my own ideas to find the holes in them. And I think that's the best that we can do in terms of staying sane on these things.

Kovid Batra: Sure. I think on that note, I think Denis would also resonate with that fact, because last time when Denis and I were talking, he mentioned about how he's helping teams implement TDD and like taking away those roadblocks time to time. So I'm sure Denis has certain questions around that, and he would like to jump in. Denis, uh, do you have any questions?

Denis Čahuk: I have a few, actually, I need your help a little bit to stay on topic. Um, so Dave mentioned something really important that sort of touched me more than the rest, which is this sort of concern for measuring individual performance. And I've been following Nicole's work as well, um, especially with SPACE metrics and what the team topology community is doing now with flow engineering. Um, there, there is a, let's say, strong interest in the community and the engineering intelligence community to measure burnout, to measure.

Dave Farley: Mm-Hmm.

Denis Čahuk: So, so the, so to clarify, do we have a high-performing team that's burnt out or do we have a healthy team that's low-performing? And to really, and to really sort of start course correct in the right areas is very difficult to measure burnout without being individual because of the need for it to be a subjective experience. Um, and I share Dave's concern where the productivity metrics are being put in the same bucket as the psychological safety and burnout research. So, I'm wondering when you're dealing with teams, because I see this with product engineering, I see this with TDD, I see this with engineering leaders who are just resistant to this idea of, you know, are we burned out? Are we just tired and we're following the right process? Or is the process correct, but it's being implemented incorrectly? How do you, how do you navigate this rift? I mean, specifically, do you find any quick, uh, lagging indicators from the DORA metrics to help you a little bit, like to cajole the conversation a little bit more? Um, or do you go to other metrics, like SPACE metrics, et cetera, to sort of, or surveying to help you start some kind of continuous delivery initiative? So a lot of teams who are not doing CD, they do complain about burnout when they're sort of being asked to start just measuring everything, just out of, um, out of, I would say, fatigue.

Dave Farley: Yeah, and, and, uh, and, uh, it gets to the, uh, Matt and Manuel's thing from the team, the Team Topologies guys, you know, uh, uh, description of cognitive load. I know it's not their, their, their idea originally, but, but, but applying it to software teams. It's, it, I, I think burnout is primarily a matter of, a mix of cognitive load and excessive cognitive load and the freedom to direct your own destiny within a team, you know? You need, you need kind of the Daniel Pink thing, autonomy, mastery and purpose. You need freedom to do a good job. You need enough scope to be, and, and that those are the kinds of things that I think are important in terms of measuring high-performance teams. I think that it's a false correlation. Um, I know that recent versions of the, the DORA reports have thrown up some, what seemed to me to be, um, counterintuitive findings. So people saying things like working with continuous integration has, is correlated with increased levels of burnout. That makes no sense to me. I put this to, to Nicole when I spoke to her as well, and she was a little skeptical of that too, in terms of the methodology for collecting the data. That's no, it's no aspersion on the people. We all get these things wrong from time to time, but I'm distrustful of that result. But if that is the result, you know, I've got to change my views on things. But my experience, and that's in the absence of, of hard data, except that previous versions of DORA gave us hard data and now the finding seems to have changed. But my experience has been that teams that are good at continuous delivery don't burn out, because it's, it's sustainable. It's long-term sustainable. The LMAX team that, that I led in the beginning of that team have been going, how long is it now? Uh, about 15 years. And those, those people weren't burning, people weren't burning out, you know, and they're producing high-quality software still, um, and their process is working still. Um, so I I'm not, I, I think that mostly burnout is a symptom of something being wrong. Um, and something being wrong in terms of too much cognitive load and not enough control of your own destiny within the team. Now, that's complicated stuff to do well, and it gets into some of the, for want of a better term, softer things, the less technical aspects of organizing teams and leading teams and so on. So we need leaders that are inspirational, that can kind of set a vision and a direction, and also demonstrating the, the right behavior. So going home on time, not, not working all hours and, you know, not telling people off if things go wrong, if it's not their fault, and all these kinds of things. So we need.. The best teams in my experience, take a lot of personal responsibility for their work, but that's, that's doing it themselves. That's not externally forced on them, and that's a good thing because that makes you both be prouder of the things that you do and more committed to doing a good job, which is in everybody's interest.

So, so I think there's, I think there's quite a lot to this. And again, it's, none of it's easy, but I think that shaping to be able to keep our software in a releasable state and working in small steps, gathering feedback, focusing on learning all of those techniques, the kind of things that I talk about all the time are some of the tools that help us to at least have a better chance of reducing burnout. Now that, there are always going to be some individuals in any system that get burnt out for other reasons. You get burnt out because of pressures from home or because your dog died or whatever it might be. Um, but, you know, we need, we need to treat this stuff seriously because we need to take care of people even if that's only for pragmatic commercial reasons, that we don't want to burn people because that's not going to be good for us long term as an industry. I, I, I, that's not more the primary reason why I would do it. But if I'm talking to a hard-nosed commercial person, I still think it's in their interest to treat people well. And so, and so we need to be cautious of people and more caring of people in the workplace. It's one of the things that I think that ensemble programming, whether it's pairing or mobbing, are significantly better for, and probably that's counterintuitive to many people, because there's a degree to which pair programming in particular applies a bit of extra pressure. You're a bit more on your game. You get a bit more, more tired at the end of each day's work, but you also build better friendships amongst your, your, your team workers and you learn from one another more effectively and you can depend on one another. If you're having a bad day, your, your, your pair might pick up the pace and be, you know, sustaining productivity or whatever else. There are all these kinds of subtle complex interactions that go on to producing a healthy workspace where, where people can keep at it for a long, you know, a long time, years at a time. And I, I think that's really important.

I worked at a company called ThoughtWorks in, in the early 2000s, and during that period, ThoughtWorks and ThoughtWorks in London in particular where I worked, where I think some of the thought leaders in agile thinking, we were pushing the boundaries of agile projects at that time and doing all sorts of interesting things. So we experimented a lot. We tried out lots of different, you know, leading edge, bleeding edge, often ideas in, in development. One of those, I worked on one of the early teams in London that was doing full-blown lean and applying that to software development. Um, and one of the things that we found was that that tended to, to, to burn us out a little bit over months because it just started to feel a bit like a treadmill. There was no kind of cadence to it because you just pick up a feature off the Kanban board, you'd work on that feature, you'd deliver the feature, you'd showcase the feature, you'd pick the next feature and you'd, and so on and so on and so on, and that was it. And you did that for months on end. And we were, we were, we were building good software. We were mostly having a good time, but over, over time it made us tired. So we started to think about how to make more social variants in the way in which we could do things. And we ended up doing the same thing, but also having iterations or most people would call them 'sprints' these days, of two weeks so that we could have a party at the end and celebrate the things that we did release, even though we weren't committing to what we'd release in the next two weeks. And, you know, we'd have some cake and stuff like that at the end, and all of those sorts of human things that just made it feel a little bit more different. We could celebrate our success and forget about our losses. Software development is a human endeavor. Let's not forget that and not try and talk, turn us into cogs in a machine. Let's treat us like human beings. Sorry. I'm off-road. I'm not sure if I answered your question.

Denis Čahuk: This is great. This is great, Dave. No need to apologize. We're enjoying this and I think our audiences as well.

Kovid Batra: I'm sure. All right. So, Denis, uh, do you have any other question?

Denis Čahuk: Well, I would like to follow up with what the story with the, with the ThoughtWorks story that Dave just mentioned You know, you mentioned you had evidence of high performance in that team. You know, we tend to forget that lean is primarily a product concern, not an engineering concern. So it sort of has to go through the ringer and to make sure, you know, does it apply to software engineering as well? And I have similar findings with things like lean, things like Kanban, particularly Scrum or the bad ways of doing Scrum is that it is, it can, it can show evidence of high performance, but not sustainably due to its lack of social component. And the retrospectives are a lame excuse at social components. It's just forcing people to judge each other and usually produces negative results rather than positive ones. So I'm wondering, you just mentioned this two-week iteration cycle for increments, but also you're leaning towards small batches. Are you still adamant on like this two-week barrier for social engagement? So, so, so what we There does seem to be a difference.

Dave Farley: Yeah, so, so, so what we did is that we retained the lean kind of Kanban style planning. We just kept that as it was, but we kind of overlaid a two-week schedule where we would have a kickoff meeting at the start of an iteration, and we would have a little retrospective at the end of an iteration and we, you know, we would talk about the work that we did over that period. So, so we had this, this kind of different cycle and that was purely human stuff. It wasn't even visible really outside of the team. It was just the way that we organized our work so that we could just look ahead for, for, for what's coming downstream as far as our Kanban board said today, and look back at what, what, what we'd, you know, what we delivered over the pre, you know, the previous iteration. It was just that kind of thing. And that was enough to give us this more human cycle, you know, because we could be, we could be looking forward to, so I'm releasing this feature, we're nearly at the end, you know, we'll talk about that tomorrow or whatever else it is, you know, and it was just nice to kind of reconnect with the rest of the team in that way. And it just, we used it essentially, I suppose you could pragmatically look at it as just as a meeting schedule for, for, for the team-level stuff. I suppose you could look at it like that, but it was, it felt like a bit more, more than that to us. But I've, by default, if I'm in a position to control these things, that's how I've organized teams ever since. And that, that's how, that's how we worked at LMAX where we built our financial exchange. That's the organization that's been going for 15 odd years, um, doing this real high-performance version of continuous delivery.

Denis Čahuk: But to pick your brain, uh, Dave, sorry to interject. When you said, you separated out the work cycles from the social cycles, that does involve daily deployments, right? Like daily pairing, daily deployments. So the releases were separate from the meeting, uh, routine.

Dave Farley: Yes. Yeah, so, so, so we, we were, we were doing the, we were doing the, the, the, the Kanban continuous delivery kind of thing of when a feature was finished, it was ready to go. So, so we were working that way. Um, there was some limitations on that sometimes, but, but, but pretty much that, that's a very close approximation have been an accurate statement, at least. Um, so, so we, we were working that way. Yeah. So we'd really, we'd essentially release on demand. We'd, we'd release when, you know, at every point when we were ready. And that was more often, usually, than once every two weeks. So the releases weren't, weren't forced to be aligned with those two week schedules. So it wasn't a technical thing at all. It was, uh, it was primarily a team social thing, but, but it worked. It worked very well.

Denis Čahuk: I really liked the brief mention about SPACE and Nicole's other work. Kovid and I are very active in the Google community. It's sort of organizing DORA-related events. And Google does have a very heavy interest in measuring well-being, measuring burnout, or just, you know, trying to figure out whether engineers and managers are actually really contributing or whether they're just slowing things down. And it's very hard to just judge from DORA metrics alone, or at least to get a clearer picture. Um, is there anything else you use for situational awareness? What would you recommend for either evidence of micromanagement, or maybe the team wants to do TDD, but there's sort of an anti-pairing stigma, if you have to, how would you approach, um, the sort of more survey-oriented, SPACE-oriented?

Dave Farley: From my experience, and I'm saying that with reservations, not with not, not, not boasting. I'm not saying because I've got great experience, but, but from my experience, I, I'm a little bit wary of trying to find quantity of ways of evaluating those things. These are very human things. So stuff like some of the examples that you mentioned, I, I've spent a significant proportion of my career as a leader of technical teams and I've always thought that it was a failure on my part as a leader of a technical team if I don't know, notice that somebody's struggling or that somebody's not pulling their weight or, or I haven't got the kind of relation, relationship where the team, if I, if I don't, if I don't know something, the team doesn't come and tell me and then I can help. I'm kind of in a weird position, for example, I'm in a slightly weird position in terms of career reviews. I think that as a manager or a leader, if you don't know the stuff that you find out in a review, you're not doing your job. You should be knowing that stuff all of the time. And it's kind of the Gemba thing. It's kind of walking around and being with the team. It's it's spending time and understanding the team as a member of the team because that's what you are. You're not outside it. You're not different. You're a member of the team, so you should feel part of that and you should be there to help, help people guide their careers and steer them in the right direction of doing better and doing, doing good things from their point of view and from the organization's point of view. But to do that, you've got, you've got to understand a little bit about what's going on. And that feels like one of those very, very human things. It's about empathy, and it's about understanding. It's about communication, and it's about trust between, between the people. And I'm not quite sure how well you can quantify that stuff.

Denis Čahuk: I coach teams primarily through this kind of engagement, to rebuild trust.

Dave Farley: Yes.

Denis Čahuk: So I have found I have zero success rate in adopting TDD if the team isn't prepared to pair on it.

Dave Farley: Yeah.

Denis Čahuk: Once the team is pairing, once the team is assembling, TDD, continuous delivery, trunk-based\ development, no problem. Once they're prepared to sort of invest time into each other, just form friendships or if nothing else, cordial acquaintances, sort of, we can sort of, bridge that gap of, well, I want you to write a test so that he can go home and spend time with his kids without worrying about deployment. So that, that is the ulterior motive, not that there is some like, you know, fairytale fashion metric to tick a box on.

Dave Farley: Yeah.

Denis Čahuk: Um, since you mentioned quantitative metrics, to sort of backtrack a little bit on that and tie it together with TDD, did you find any lagging indicators of a team that, that did adopt TDD after you came in that, you know, what, what are the key metrics that are getting better, different after TDD adoption, or maybe leading indicators or perhaps leading indicators that say, hey, this more than anything else needs attention?

Dave Farley: So, so, so, so I think, I think, I think mostly, uh, stability. So, so it's a lagging indicator, but I, I think that's the measure that, you know, tells us whether you're doing a good enough job on quality. And if you're not doing TDD, mostly the data says you're not doing a good enough job on quality. There's a lot of other measures that kind of reinforce that picture, but fundamentally in terms of monitoring our performance day-to-day, I think stability is the best tool for that. Um, and, you know, so, so some, you know, so there's, I, I, I'm interested as a technologist from a technical point of view in some of the work that, um, Adam Thornhill, uh, uh, and code scene are doing in terms of red code and things like that. So patterns of use in code, the stuff that changes a lot and monitoring the stuff that changes a lot versus this stuff that, you know, where, where defects happen and all that kind of stuff. And so, you know, the crossover between sort of cyclomatic complexity and other measures of complexity in code and the need to change it a lot and all that kind of stuff. I think that's all interesting and kind of, but I see that as reinforcing this view of how important quality is. And fundamentally, we need to find ways of doing less work, managing our cognitive load to achieve higher quality, and that's what TDD does. So TDD isn't the end in itself. It's, it's a tool that gives us, that pushes us in the direction of the end that matters, which is building high-quality software and maintaining our ability to change it. And that's, again, that's what TDD does. So, so, so I think that TDD influences software in some deep ways that people that don't practice TDD miss all of the time.

And it's linked to lots of other practices. Like you said, um, you know, pairing is a great way of helping to introduce TDD, uh, particularly for our people that already know how to do TDD in the team. That's, that's the way that you spread it, certainly, but it's, I can't, I can't think of many things that, that, as I say, I'm wary of measures. I tend to either use tactical measures that just seem right in the context of what we're doing now, sort of treating each thing as an experiment and trying to figure out how to experiment on this thing and what do I need to measure to, to do that, or I use stability and throughput primarily.

Kovid Batra: Uh, I'll just, uh, take a pause here for all of us because, uh, we have a QnA lined up for the audience. And, uh, we will try to take like 30, 30 seconds of a break here and, uh, audience, you can get started, posting your questions. Uh, we are ready to take them.

Denis Čahuk: We already have a few comments and we had, uh,

Kovid Batra: Okay. I think, uh, we can start with the questions.

Denis Čahuk: Before we go into Paul's question. Paul has a great question. I just want to preface that by saying that not this one, the DORA-related one.

Kovid Batra: But I like this one more.

Denis Čahuk: Yes.

Kovid Batra: Dave, I think you have to answer this one. Uh, where do you get your array of t-shirts?

Dave Farley: So, so, so mostly I buy my t-shirts off a company based in Ireland called QWERTEE. "QWERTEE". And if you go to, if you go to any of my videos, there's usually a link underneath them where you can get a discount off the t-shirts because we did a deal with QWERTEE because, because so many people commented on my t-shirts.

Denis Čahuk: Great t-shirts. Well done.

Kovid Batra: Yeah. Denis.

Denis Čahuk: I just wanted to, I just wanted to preface Paul's other question regarding how to measure that, you know, Kovid and I are very active in the DORA communities on the Google, Google group, and by far the most asked questions are, how do I precisely measure X? How do I correctly measure this? My team does not follow continuous delivery. We have feature branches. How do I correctly measure this metric, that metric? Before we go into too much detail, I just wanna emphasize that if you're not measuring, if you're not doing continuous delivery, then the metrics will tell you that you should probably be doing continuous delivery. And..

Dave Farley: Yeah.

Denis Čahuk: The ulterior motive is how can we get to continuous delivery sooner? Not how can we correctly measure DORA metrics and continue doing feature branching. Yeah, that's that is generally the most trending conversation topic on these groups. And I just want to take a lot of time to sort of nail, like the, it's about the business. It's about continuous delivery, running experiments quickly, smoother, safely, sustainably, rather than directly measuring any kind of dysfunctional workflow. Or even if you can judge that your workflow is bad because the metrics don't track properly, which is usually where people turn towards DORA metrics.

Dave Farley: Yeah, I would add to that as well is that even if you, even if you get the measures and you use the measures, you're still not going to convince people it's the measures enough alone aren't enough. You need, you need to approach this from a variety of different directions to start convincing people to change their minds over things, and that's without being disrespectful from those, of those people that differ in terms of their viewpoints, because it's hard to change your mind about something if you've, if you've made a career working in a certain way, it's hard to change the things that from the things that you've learned. Um, so this is challenging, and that's the downside of continuous delivery. It works better than anything else. It's the most fun way of organizing our work. It does tend to eliminate, in my experience, burnout in teams, all of these good things. You build better software more quickly working this way. But it's hard to adopt when you're not, when you've not done it before. Everybody that I know that's tried likes it better, but it's hard to make the change.

Denis Čahuk: It's a worthwhile change that manages a lot of stress and burnout, but that doesn't mean there aren't difficult conversations along the way.

Dave Farley: Sure.

Kovid Batra: All right, uh, moving on to the next one. Uh, how do you find the right balance between speed and quality while delivering software?

Dave Farley: The DORA metrics answer this question. There is no trade off, so there is no need to balance. If you want more speed, you need to build with higher quality. If you want more quality, you need to build faster. So let's just, let's just explain that a little bit because I think it's useful to just have this idea in mind because, because we have to defend ourselves because it seems, it seems like a reasonable idea that there's a trade off between speed and quality. It's just not true. But it seems like a reasonable idea. So, so if I build bad software this week and then next week, I've got a load more pressure on me to build next week's work, next week, I'm going to have all of that pressure plus all of the cost of the bad software that I wrote this week. So it's obviously more efficient if I build good software this week and then I don't have that work next week and then I could build good software next week as well. And what that plays out to is that that's where the 44 percent comes from. That's where the increase in productivity comes from. If we concentrate and organize our work to build higher quality software, we save time. We don't, we don't waste, we don't, it doesn't cost time.

Now there's a transition period. If you're busy working in a poor software development environment, that's building crap software, then, you know, it's going to take you a while to learn some of these things. So there's, there's an activation energy to get better at building software. But once you do, you will be going faster and building higher quality software at the same time because they come together. So what do we mean by fast when we talk about going fast if you want high quality software? Fundamentally, that's about working in smaller steps. So we want to organize our work into much smaller steps so that after each small step, we can evaluate where we are and whether what, whether that step that we took was, was a good one. And that's in all kinds of ways. Does my software do what I think it does? Does it do what the customer wants it to do? Is it making money in production or whatever else it is? So, so all of these things, you know, these are learning points and we need to build that more experimental mindset into the, in deep, into the way that we work.

And the smart thing to do. To optimize all of this is to make it easy to do the right things. It makes it, make it easy for us to carry out these small steps in these experiments. And that's what continuous delivery does. That's what the deployment pipeline fundamentally is for. It's an experimental platform that will give us a definitive statement on the releasability of our software multiple times per day. And that makes it easier then to, to work, to work in these small steps and do that quickly and, and get high quality results back.

Kovid Batra: Totally makes sense. Moving on, uh, Agustin, uh, why is it so, why is it so important in your opinion to differentiate between continuous delivery, continuous deployment, and how that affects the delivery process performance, also known as the DORA metrics?

Dave Farley: So, so, so, so let me first differentiate between them and then explain why I think it matters. So, so continuous delivery is working so that our software is always in a releasable state. Continuous deployment is built on top of continuous delivery. And if all of your tests pass, you just push the change out automatically into production. And that's a really, really good thing. If you can get, if you can get to that point where you can release all of the time small changes, that's probably the best way of getting this, optimising to get this really fast feedback, all the way out to your end users. Now the problem is, is that there are some kinds of software where that doesn't make any sense. There are some kinds of software for a variety of different kinds of reasons, depending on the technology, the regulation, um, real practical limitations for some reason, why we can't do that. So, Tesla are a continuous delivery company. But part of what they are continuously, continuously delivering is software embodied as silicon burnt into devices in the car. There's physics involved in burning the silicon. So you can't always release every change immediately that the software is, the software is done. That's not practical. So you have to manage that slightly differently. Uh, one of my clients, um, Siemens build medical devices and so, within the regulatory framework for medical devices that can kill people, you're not allowed to release them all of the time into production. And so, continuous delivery is the foundational idea but continuous deployment is kind of the, the limit, I suppose of where you can get to. If you're Amazon, continuous, continuous deployment makes a huge amount of sense. Amazon are pushing out changes. I think it's currently 1. 5 changes per second. It might be more than that. It might be five changes per second. Something like that. Something ridiculous like that. But that's what they're doing. And so they're able to move ridiculously fast and learn ridiculously quickly. And so build better software. I think you can think of it from a more internally focused viewpoint as that they each optimize for slightly different things.

Continuous delivery gives us feedback on whether we are, um, building things right and continuous deployment gives us feedback on whether we're building the right things. So we learn more about our product from continuous deployment by getting it into the hands of real users, monitoring that and understanding their impact. We get, and we can't get that kind of feedback any other way really than getting out to real users. We don't learn those lessons until real users are really using it. Continuous delivery though, gives us feedback on, does this do what we think it's doing? Um, is it good quality? Is it fast enough? Is it resilient enough? All of those kinds of things. We can measure those things. And we can know those before we release. So, they are slightly different things. And they do, they do balance off in different ways. They give us different levels of value. There's an excellent book that's recently been released on continuous deployment. Um, I've forgotten the name of the author. Valentina, somebody, I think. Um, but I wrote the foreword, so I should remember the name of the author. I'm very embarrassed, but it's, it's, it's a really good book, and it goes into lots of detail about continuous deployment as distinct from continuous delivery. I think, but I suppose I would say this, wouldn't I? I think that continuous delivery is the more foundational practice here, and I think that depending on your viewpoint, I think this is one of the very, very few ideas where, where Jez Humble and I would, would come at this from slightly different perspectives. I tended, I've tended to spend the latter part of my career working in environments where continuous deployment wasn't practical. I couldn't, I was never going to get my clients to, to, to do it in, in, in the environments in which they were building things. And sometimes they couldn't even if they wanted to. Um, I think Jez has worked in environments where continuous deployment was a little easier. And so that seems more natural. And so I think that kind of is why, um, some of the DORA metrics, for example, measure the efficiency based on assumptions, really, of continuous deployment.

Um, so I think, I think continuous deployment is the right target to aim for. You want to be able to release as frequently as is practicable, given the constraints on you, and you want to kind of push at the boundaries of those constraints where you can. So, for example, working with Siemens, we weren't allowed to release software into production of medical systems in clinical settings, but we could release much more frequently to non-clinical settings. So we did that, so we identified some non-clinical settings, and we released frequently to those places, in university hospitals, for example, and so on.

Kovid Batra: So I think it's almost time. Uh, and, uh, we do have more questions, but just because the stream is for an hour, uh, it's going to end. So we'll take those questions offline. Uh, I'll email the answers to you. Uh, audience, please don't be disappointed here. It's just in the interest of time that we'll have to stop here. Thank you so much, Dave, Denis, for this amazing, amazing session. It was nice talking to you and learning so much about CD, TDD, engineering metrics from you. Thank you so much once again.

Dave Farley: It's a pleasure. Thank you. Bye-bye. Thanks everyone.

Denis Čahuk: Thanks!

Project success with devops metrics

Measuring Project Success with DevOps Metrics

Are you feeling unsure if your team is making real progress, even though you’re following DevOps practices? Maybe you’ve implemented tools and automation but still struggle to identify what’s working and what’s holding your projects back. You’re not alone. Many teams face similar frustrations when they can’t measure their success effectively.

But here’s the truth: without clear metrics, it’s nearly impossible to know if your DevOps processes are driving the results you need. Tracking the right DevOps metrics can make all the difference, offering insights that help you streamline workflows, fix bottlenecks, and make data-driven decisions.

In this blog, we’ll dive into the essential DevOps metrics that empower teams to confidently measure success. Whether you’re just getting started or looking to refine your approach, these metrics will give you the clarity you need to drive continuous improvement. Ready to take control of your project’s success? Let’s get started.

What Are DevOps Metrics?

DevOps metrics are statistics and data points that correlate to a team's DevOps model's performance. They measure process efficiency and reveal areas of friction between the phases of the software delivery pipeline. 

These metrics are essential for tracking progress toward achieving overarching goals set by the team. The primary purpose of DevOps metrics is to provide insight into technical capabilities, team processes, and overall organizational culture. 

By quantifying performance, teams can identify bottlenecks, assess quality improvements, and measure application performance gains. Ultimately, if you don’t measure it, you can’t improve it.

Key Categories of DevOps Metrics

The DevOps Metrics has these primary categories: 

  • Software Delivery Metrics: Measure the speed and efficiency of software delivery.
  • Stability Metrics: Assess the reliability and quality of software in production.
  • Operational Performance Metrics: Evaluate system performance under load.
  • Security Metrics: Monitor vulnerabilities and compliance within the software development lifecycle.
  • Cost Efficiency Metrics: Analyze resource utilization and cost-effectiveness in DevOps practices.

Understanding these categories helps organizations select relevant metrics tailored to their specific challenges.

Why Metrics Matter: Driving Measurable Success with DevOps

DevOps is often associated with automation and speed, but at its core, it is about achieving measurable success. Many teams struggle with measuring their success due to inconsistent performance or unclear goals. It's understandable to feel lost when confronted with vast amounts of data and competing priorities.

However, the right metrics can simplify this process. 

They help clarify what success looks like for your team and provide a framework for continuous improvement. Remember, you don't have to tackle everything at once; focusing on a few key metrics can lead to significant progress.

Key DevOps Metrics to Track for Success

To effectively measure your project's success, consider tracking the following essential DevOps metrics:

Deployment Frequency

This metric tracks how often your team releases new code. A higher frequency indicates a more agile development process. Deployment frequency is measured by dividing the number of deployments made during a given period by the total number of weeks/days. One deployment per week is standard, but it also depends on the type of product.

For example, a team working on a mission-critical financial application may aim for daily deployments to fix bugs and ensure system stability quickly. In contrast, a team developing a mobile game might release updates weekly to coincide with the app store's review process.

Lead Time for Changes 

Measure how quickly changes move from development to production. Shorter lead times suggest a more efficient workflow. Lead time for changes is the length of time between when a code change is committed to the trunk branch and when it is in a deployable state, such as when code passes all necessary pre-release tests.

Consider a scenario where a developer submits a bug fix to the main codebase. The change is automatically tested, approved, and deployed to production within an hour. This rapid turnaround allows the team to quickly address customer issues and maintain a high level of service.

Change Failure Rate

This assesses the percentage of changes that cause issues requiring a rollback. Lower rates indicate better quality control. The change failure rate is the percentage of code changes that require hot fixes or other remediation after production, excluding failures caught by testing and fixed before deployment.

Imagine a team that deploys 100 changes per month, with 10 of those changes requiring a rollback due to production issues. Their change failure rate would be 10%. By tracking this metric over time and implementing practices like thorough testing and canary deployments, they can work to reduce the failure rate and improve overall stability.

Mean Time to Recovery (MTTR)

Evaluate how quickly your team can recover from failures. A shorter recovery time reflects resilience and effective incident management. MTTR measures how long it takes to recover from a partial service interruption or total failure, regardless of whether the interruption is the result of a recent deployment or an isolated system failure.

In a scenario where a production server crashes due to a hardware failure, the team's MTTR is the time it takes to restore service. If they can bring the server back online and restore functionality within 30 minutes, that's a strong MTTR. Tracking this metric helps teams identify areas for improvement in their incident response processes and infrastructure resilience.

These metrics are not about achieving perfection; they are tools designed to help you focus on continuous improvement. High-performing teams typically measure lead times in hours, have change failure rates in the 0-15 percent range, can deploy changes on demand, and often do so many times a day.

Common Challenges When Measuring DevOps Success

While measuring success is essential, it's important to acknowledge the emotional and practical hurdles that come with it:

Resistance to change 

People often resist change, especially when it disrupts established routines or processes. Overcoming this resistance is crucial for fostering a culture of improvement.

For example, a team that has been manually deploying code for years may be hesitant to adopt an automated deployment pipeline. Addressing their concerns, providing training, and demonstrating the benefits can help ease the transition.

Lack of time

Teams frequently find themselves caught up in day-to-day demands, leaving little time for proactive improvement efforts. This can create a cycle where urgent tasks overshadow long-term goals.

A development team working on a tight deadline may struggle to find time to optimize their deployment process or write automated tests. Prioritizing these activities as part of the sprint planning process can help ensure they are not overlooked.

Complacency

Organizations may become complacent when things seem to be functioning adequately, preventing them from seeking further improvements. The danger lies in assuming that "good enough" will suffice without striving for excellence.

A team that has achieved a 95% test coverage rate may be tempted to focus on other priorities, even though further improvements could catch additional bugs and reduce technical debt. Regularly reviewing metrics and setting stretch goals can help avoid complacency.

Data overload

With numerous metrics available, teams might struggle to determine which ones are most relevant to their goals. This can lead to confusion and frustration rather than clarity.

A large organization with dozens of teams and applications may find itself drowning in DevOps metrics data. Focusing on a core set of key metrics that align with overall business objectives and tailoring dashboards for each team's specific needs can help manage this challenge.

Measuring success

Determining what success looks like and how to measure it in a continuous improvement culture can be challenging. Setting clear goals and KPIs is essential but often overlooked.

A team may struggle to define what "success" means for their project. Collaborating with stakeholders to establish measurable goals, such as reducing customer support tickets by 20% or increasing revenue by 5%, can provide a clear target to work towards.

If you're facing these challenges, remember that you are not alone. Start by identifying the most actionable metrics that resonate with your current goals. Focusing on a few key areas can make the process feel more manageable and less daunting.

How to Use DevOps Metrics for Continuous Improvement

Once you've identified the key metrics to track, it's time to leverage them for continuous improvement:

Establish baselines: Begin by establishing baseline measurements for each metric you plan to track. This will give you a reference point against which you can measure progress over time.

For example, if your current deployment frequency is once every two weeks, establish that as your baseline before setting a goal to deploy weekly within three months.

Set clear objectives: Define specific objectives for each metric based on your baseline measurements. For instance, if your current deployment frequency is once every two weeks, aim for weekly deployments within three months.

Implement feedback loops: Create mechanisms for gathering feedback from team members about processes and tools regularly used in development cycles. This could be through retrospectives or dedicated feedback sessions focusing on specific metrics.

After each deployment, hold a brief retrospective to discuss what went well, what could be improved, and any insights gained from the deployment metrics. Use this feedback to refine processes and inform future improvements.

Analyze trends: Regularly analyze trends in your metrics data rather than just looking at snapshots in time. For example, if you notice an increase in change failure rate over several weeks, investigate potential causes such as code complexity or inadequate testing practices.

Use tools like Typo to visualize trends in your DevOps metrics over time. Look for patterns and correlations that can help identify areas for improvement. For instance, if you notice that deployments with more than 50 commits tend to have higher failure rates, consider breaking changes into smaller batches.

Encourage experimentation: Foster an environment where team members feel comfortable experimenting with new processes or tools based on insights gained from metrics analysis. Encourage them to share their findings with others in the organization.

If a developer discovers a new testing framework that significantly reduces the time required to validate changes, support them in implementing it and sharing their experience with the broader team. Celebrating successful experiments helps reinforce a culture of continuous improvement.

Celebrate improvements: Recognize and celebrate improvements achieved through data-driven decision-making efforts—whether it's reducing MTTR or increasing deployment frequency—this reinforces positive behavior within teams.

When a team hits a key milestone, such as deploying 100 changes without a single failure, take time to acknowledge their achievement. Sharing success stories helps motivate teams and demonstrates the value of DevOps metrics.

Iterate regularly: Continuous improvement is not a one-time effort; it requires ongoing iteration based on what works best for your team's unique context and challenges encountered along the way.

As your team matures in its DevOps practices, regularly review and adjust your metrics strategy. What worked well in the early stages may need to evolve as your organization scales or faces new challenges. Remain flexible and open to experimenting with different approaches.

By following these steps consistently over time, you'll create an environment where continuous improvement becomes ingrained within your team's culture—ultimately leading toward greater efficiency and higher-quality outputs across all projects. 

Overcoming Obstacles with Typo: A Powerful DevOps Metrics Tracking Solution

One tool that can significantly ease the process of tracking DevOps metrics is Typo—a user-friendly platform designed specifically for streamlining metric collection while integrating seamlessly into existing workflows:

Key Features of Typo

Intuitive interface: Typo's user-friendly interface allows teams to easily monitor critical metrics such as deployment frequency and lead time for changes without extensive training or onboarding processes required beforehand.

For example, the Typo dashboard provides a clear view of key metrics like deployment frequency over time so teams can quickly see if they are meeting their goals or if adjustments are needed.

DORA Metrics in Typo

Automated data collection

By automating data collection processes through integrations with popular CI/CD tools like Jenkins or GitLab CI/CD pipelines—Typo eliminates manual reporting burdens placed upon developers—freeing them up so they can focus more on delivering value rather than managing spreadsheets!

Typo automatically gathers deployment data from your CI/CD tools so developers save time while reducing human error risk associated with manual data entry—allowing them instead to concentrate solely on improving results achieved through informed decision-making based upon actionable insights derived directly from their own data!

Real-time performance dashboards

Typo provides real-time performance dashboards that visualize key metrics at a glance, enabling quick decision-making based on current performance trends rather than relying solely upon historical data points!

The Typo dashboard updates in real time as new deployments occur, giving teams an immediate view of their current performance against goals. This allows them to quickly identify and address any issues arising. 

Customizable alerts & notifications

With customizable alerts set up around specific thresholds (e.g., if the change failure rate exceeds 10%), teams receive timely notifications that prompt them to take action before issues escalate further down production lines!

Typo allows teams to set custom alerts based on specific goals and thresholds—for example, receiving notification if the change failure rate rises above 5% over three consecutive deployments, helping catch potential issues early before they cause major problems. 

Integration capabilities

Typo effortlessly integrates with various project management tools (like Jira) alongside monitoring solutions (such as Datadog), providing comprehensive insights into both development processes and operational performance simultaneously.

Using Typo empowers organizations simplifying metric tracking without overwhelming users allowing them instead concentrate solely upon improving results achieved through informed decision-making based upon actionable insights derived directly from their own data. 

Embracing the DevOps Metrics Journey

As we conclude this discussion, measuring project success, effective DevOps metrics serve invaluable strategies driving continuous improvement initiatives while enhancing collaboration efforts among various stakeholders involved throughout every stage—from development through deployment until final delivery. By focusing specifically on key indicators like deployment frequency alongside lead time changes coupled together alongside change failure rates mean time recovery—you'll gain deeper insights into identifying bottlenecks while optimizing workflows accordingly. 

While challenges may arise along this journey towards achieving excellence within software delivery processes—tools like Typo combined alongside supportive cultures fostered throughout organizations will help navigate these obstacles successfully unlocking full potential inherent within each team member involved. 

So take those first steps today! 

Start tracking relevant metrics now—watch closely improvements unfold before eyes transforming not only how projects executed but also elevating overall quality delivered across all products released moving forward. 

Join for a demo with Typo to learn more. 

DORA Metrics from Typo

DORA Metrics Explained: Insights from Typo

“Why does it feel like no matter how hard we try, our software deployments are always delayed or riddled with issues?”

Many development teams ask this question as they face the ongoing challenges of delivering software quickly while maintaining quality. Constant bottlenecks, long lead times, and recurring production failures can make it seem like smooth, efficient releases are out of reach.

But there’s a way forward: DORA Metrics. 

By focusing on these key metrics, teams can gain clarity on where their processes are breaking down and make meaningful improvements. With tools like Typo, you can simplify tracking and start taking real, actionable steps toward faster, more reliable software delivery. Let’s explore how DORA Metrics can help you transform your process.

What are DORA Metrics?

DORA Metrics consist of four key indicators that help teams assess their software delivery performance:

  • Deployment Frequency: This metric measures how often new releases are deployed to production. High deployment frequency indicates a responsive and agile development process.
  • Lead time for Changes: This tracks the time it takes for a code change to go from commit to deployment. Short lead times reflect an efficient workflow and the ability to respond quickly to user feedback.
  • Mean Time to Recovery (MTTR): This indicates how quickly a team can recover from a failure in production. A lower MTTR signifies strong incident management practices and resilience in the face of challenges.
  • Change Failure Rate: This measures the percentage of deployments that result in failures, such as system outages or degraded performance. A lower change failure rate indicates higher quality releases and effective testing processes.

These metrics are essential for teams striving to deliver high-quality software efficiently and can significantly impact overall performance.

Challenges teams commonly face

While DORA Metrics provide valuable insights, teams often encounter several common challenges:

  • Data overload and complexity: Tracking too many metrics can lead to confusion and overwhelm, making it difficult to identify key areas for improvement. Teams may find themselves lost in data without clear direction.
  • Misaligned priorities: Different teams may have conflicting goals, making it challenging to work towards shared objectives. Without alignment, efforts can become fragmented, leading to inefficiencies.
  • Fear of failure: A culture that penalizes mistakes can hinder innovation and slow down progress. Teams may become risk-averse, avoiding necessary changes that could enhance their delivery processes.

Breaking down the 4 DORA Metrics

Understanding each DORA Metric in depth is crucial for improving software delivery performance. Let's dive deeper into what each metric measures and why it's important:

Deployment Frequency

Deployment frequency measures how often an organization successfully releases code to production. This metric is an indicator of overall DevOps efficiency and the speed of the development team. Higher deployment frequency suggests a more agile and responsive delivery process.

To calculate deployment frequency:

  • Track the number of successful deployments to production per day, week, or month.
  • Determine the median number of days per week with at least one successful deployment.
  • If the median is 3 or more days per week, it falls into the "Daily" deployment frequency bucket.
  • If the median is less than 3 days per week but the team deploys most weeks, it's considered "Weekly" frequency.
  • Monthly or lower frequency is considered "Monthly" or "Yearly" respectively.

The definition of a "successful" deployment depends on your team's requirements. It could be any deployment to production or only those that reach a certain traffic percentage. Adjust this threshold based on your business needs.

Read more: Learn How Requestly Improved their Deployment Frequency by 30%

Lead Time for Changes

Lead time for changes measures the amount of time it takes a code commit to reach production. This metric reflects the efficiency and complexity of the delivery pipeline. Shorter lead times indicate an optimized workflow and the ability to respond quickly to user feedback.

To calculate lead time for changes:

  • Maintain a list of all changes included in each deployment, mapping each change back to the original commit SHA.
  • Join this list with the changes table to get the commit timestamp.
  • Calculate the time difference between when the commit occurred and when it was deployed.
  • Use the median time across all deployments as the lead time metric.

Lead time for Changes is a key indicator of how quickly your team can deliver value to customers. Reducing the amount of work in each deployment, improving code reviews, and increasing automation can help shorten lead times.

Change Failure Rate (CFR)

Change failure rate measures the percentage of deployments that result in failures requiring a rollback, fix, or incident. This metric is an important indicator of delivery quality and reliability. A lower change failure rate suggests more robust testing practices and a stable production environment.

To calculate change failure rate:

  • Track the total number of deployments attempted.
  • Count the number of those deployments that caused a failure or needed to be rolled back.
  • Divide the number of failed deployments by the total to get the percentage.

Change failure rate is a counterbalance to deployment frequency and lead time. While those metrics focus on speed, change failure rate ensures that rapid delivery doesn't come at the expense of quality. Reducing batch sizes and improving testing can lower this rate.

Mean Time to Recovery (MTTR)

Mean time to recovery measures how long it takes to recover from a failure or incident in production. This metric indicates a team's ability to respond to issues and minimize downtime. A lower MTTR suggests strong incident management practices and resilience.

To calculate MTTR:

  • For each incident, note when it was opened.
  • Track when a deployment occurred that resolved the incident.
  • Calculate the time difference between incident creation and resolution.
  • Use the median time across all incidents as your MTTR metric.

Restoring service quickly is critical for maintaining customer trust and satisfaction. Improving monitoring, automating rollbacks, and having clear runbooks can help teams recover faster from failures.

By understanding these metrics in depth and tracking them over time, teams can identify areas for improvement and measure the impact of changes to their delivery processes. Focusing on these right metrics helps optimize for both speed and stability in software delivery.

If you are looking to implement DORA Metrics in your team, download the guide curated by DORA experts at Typo.

How to start using DORA Metrics effectively

Starting with DORA Metrics can feel daunting, but here are some practical steps you can take:

Step 1: Identify your goals

Begin by clarifying what you want to achieve with DORA Metrics. Are you looking to improve deployment frequency? Reduce lead time? Understanding your primary objectives will help you focus your efforts effectively.

Step 2: Choose one metric

Select one metric that aligns most closely with your current goals or pain points. For instance:

  • If your team struggles with frequent outages, focus on reducing the Change Failure Rate.
  • If you need faster releases, prioritize Deployment Frequency.

Step 3: Establish baselines

Before implementing changes, gather baseline data for your chosen metric over a set period (e.g., last month). This will help you understand your starting point and measure progress accurately.

Step 4: Implement changes gradually

Make small adjustments based on insights from your baseline data. For example:

If focusing on Deployment Frequency, consider adopting continuous integration practices or automating parts of your deployment process.

Step 5: Monitor progress regularly

Use tools like Typo to track your chosen metric consistently. Set up regular check-ins (weekly or bi-weekly) to review progress against your baseline data and adjust strategies as needed.

Step 6: Iterate based on feedback

Encourage team members to share their experiences with implemented changes regularly. Gather feedback continuously and be open to iterating on your processes based on what works best for your team.

How Typo helps with DORA Metrics 

Typo simplifies tracking and optimizing DORA Metrics through its user-friendly features:

  • Intuitive dashboards: Typo's dashboards allow teams to visualize their chosen metric clearly, making it easy to monitor progress at a glance while customizing views based on specific needs or roles within the team.
  • Focused tracking: By enabling teams to concentrate on one metric at a time, Typo reduces information overload. This focused approach helps ensure that improvements are actionable and manageable.
  • Automated reporting: Typo automates data collection and reporting processes, saving time while reducing errors associated with manual tracking so you receive regular updates without extensive administrative overhead.
  • Actionable insights: The platform provides insights into bottlenecks or areas needing improvement based on real-time data analysis; if cycle time increase, Typo highlights specific stages in your deployment pipeline requiring attention.

DORA Metrics in Typo

By leveraging Typo's capabilities, teams can effectively reduce lead times, enhance deployment processes, and foster a culture of continuous improvement without feeling overwhelmed by data complexity.

“When I was looking for an Engineering KPI platform, Typo was the only one with an amazing tailored proposal that fits with my needs. Their dashboard is very organized and has a good user experience, it has been months of use with good experience and really good support” 
- Rafael Negherbon, Co-founder & CTO @ Transfeera

Read more: Learn How Transfeera reduced Review Wait Time by 70%

Common Pitfalls and How to Avoid them

When implementing DORA Metrics, teams often encounter several pitfalls that can hinder progress:

Over-focusing on one metric: While it's essential prioritize certain metrics based on team goals, overemphasizing one at others' expense can lead unbalanced improvements; ensure all four metrics are considered strategy holistic view performance.

Ignoring contextual factors: Failing consider external factors (like market changes organizational shifts) when analyzing metrics can lead astray; always contextualize data broader business objectives industry trends meaningful insights.

Neglecting team dynamics: Focusing solely metrics without considering team dynamics create toxic environment where individuals feel pressured numbers rather than encouraged collaboration; foster open communication within about successes challenges promoting culture learning from failures.

Setting unrealistic targets: Establishing overly ambitious targets frustrate team members if they feel these goals unattainable reasonable timeframes; set realistic targets based historical performance data while encouraging gradual improvement over time.

Key Approaches to Implementing DORA Metrics

When implementing DORA (DevOps Research and Assessment) metrics, it is crucial to adhere to best practices to ensure accurate measurement of key performance indicators and successful evaluation of your organization's DevOps practices. By following established guidelines for DORA metrics implementation, teams can effectively track their progress, identify areas for improvement, and drive meaningful changes to enhance their DevOps capabilities.

Customize DORA metrics to fit your team's needs

Every team operates with its own unique processes and goals. To maximize the effectiveness of DORA metrics, consider the following steps:

  • Identify relevant metrics: Determine which metrics align best with your team's current challenges and objectives.
  • Adjust targets: Use historical data and industry benchmarks to set realistic targets that reflect your team's context.

By customizing these metrics, you ensure they provide meaningful insights that drive improvements tailored to your specific needs.

Foster leadership support for DORA metrics

Leadership plays a vital role in cultivating a culture of continuous improvement. To effectively support DORA metrics, leaders should:

  • Encourage transparency: Promote open sharing of metrics and progress among all team members to foster accountability.
  • Provide resources: Offer training and resources that focus on best practices for implementing DORA metrics.

By actively engaging with their teams about these metrics, leaders can create an environment where everyone feels empowered to contribute toward collective goals.

Track progress and celebrate wins

Regularly monitoring progress using DORA metrics is essential for sustained improvement. Consider the following practices:

  • Schedule regular check-ins: Hold retrospectives focused on evaluating progress and discussing challenges.
  • Celebrate achievements: Take the time to recognize both small and significant successes. Celebrating wins boosts morale and motivates the team to continue striving for improvement.

Recognizing achievements reinforces positive behaviours and encourages ongoing commitment, ultimately enhancing software delivery practices.

Empowering Teams with DORA Metrics

DORA Metrics offer valuable insights into how to transform software delivery processes, enhance collaboration, and improve quality; understanding these deeply and implementing them thoughtfully within an organization positions it for success in delivering high-quality efficiently.

Start small manageable changes—focus one metric at time—leverage tools like Typo support journey better performance; remember every step forward counts creating more effective development environment where continuous improvement thrives!

Webinar: ‘The Hows and Whats of DORA.' with Bryan Finster and Richard Pangborn

Typo hosted an exclusive live webinar titled 'The Hows and Whats of DORA', featuring Bryan Finster and Richard Pangborn. With over 150+ attendees, we explored how DORA can be misused and learnt practical tips for turning engineering metrics into dev team success.

Bryan Finster, Value Stream Architect at Defense Unicorns and co-author of 'How to Misuse and Abuse DORA Metrics’, and Richard Pangborn, Software Development Manager at Method and advocate for Typo, brought valuable perspectives to the table.

The discussion covered DORA metrics' implementation and challenges, highlighting the critical role of continuous delivery and value stream management. Bryan provided insights from his experience at Walmart and Defense Unicorns, explaining the pitfalls of misusing DORA metrics. Meanwhile, Richard shared his hands-on experience with implementation challenges, including data collection difficulties and the crucial need for accurate observability. They also reinforced the idea that DORA metrics should serve as health indicators rather than direct targets. Bryan and Richard offered parting advice on using observability effectively and ensuring that metrics lead to meaningful improvements rather than superficial compliance. They both emphasized the importance of a supportive culture that sees metrics as tools for improvement rather than instruments of pressure.

The event concluded with an interactive Q&A session, allowing attendees to ask questions and gain deeper insights.

P.S.: Our next live webinar is on September 25, featuring DORA expert Dave Farley. We hope to see you there!

Timestamps

  • 00:00 - Introduction
  • 00:59 - Meet Richard Pangborn
  • 02:58 - Meet Bryan Finster
  • 04:49 - Bryan's Journey with Continuous Delivery
  • 07:33 - Challenges & Misuse of DORA Metrics
  • 20:55 - Richard's Experience with DORA Metrics
  • 27:43 - Ownership of MTTR & Measurement Challenges
  • 28:27 - Cultural Resistance to Measurement
  • 29:37 - Team Metrics vs Individual Metrics
  • 31:29 - Value Stream Mapping Insights
  • 33:56 - Q&A Session
  • 40:19 - Setting Realistic Goals with DORA Metrics
  • 45:31 - Final Thoughts & Advice

Links and Mentions 

Transcript

Kovid Batra: Hi, everyone. Thanks for joining in for our DORA exclusive webinar, The Hows and Whats of DORA, powered by Typo. I'm Kovid, founding member at Typo and your host for today's webinar. With me, we have two special people. Please welcome the DORA expert for tonight, Bryan Finster, who is an exceptional Value Stream Architect at Defense Unicorns and the co-author of the ebook, 'How to Misuse and Abuse DORA Metrics', and one of our product mentors, and Typo advocates, Richard Pangborn, who is a Software Development Manager at Method. Thanks, Bryan. Thanks, Rich, for joining in. 

Bryan Finster: Thanks for having me. 

Richard Pangborn: Yeah, no problem. 

Kovid Batra: Great. So, like, before we, uh, get started and discuss about how to implement DORA, how to misuse DORA, uh, Rich, you have some questions to ask, uh, we would love to know a little bit about you both. So if you could just spare a minute and tell us about yourself. So I think we can get started with you, Rich. Uh, and then we can come back to Bryan. 

Richard Pangborn: Sure. Yeah, sounds good. Uh, my name is Richard Pangborn. I'm the Software Developer Manager here at Method. Uh, I've been a manager for about three years now. Um, but I do come from a Tech Lead role of five or more years. Um, I started here as a junior developer when we were just in the startup phase. Um, went through the series funding, the investments, the exponential growth. Today we're, you know, over a 100-person company with six software development teams. Um, and yeah, Typo is definitely something that we've been using to help us measure ourselves and succeed. Um, some interesting things about myself, I guess, is I was part of the company and team that succeeded when we did a Intuit hackathon. Um, it was pretty impactful to me. Um, We brought this giant check, uh, back with us from Cali all the way to Toronto, where we're located. Uh, we got to celebrate with, uh, all of the company, everyone who put in all the hard and hard work to, to help us succeed. Um, that's, that's sort of what pushed me into sort of a management path to sort of mentor and help those, um, that are junior or intermediate, uh, have that same sort of career path, uh, and set them up for success.

Kovid Batra: Perfect. Perfect. Thanks, Richard. And something apart from your professional life, anything that you want to share with the audience about yourself? 

Richard Pangborn: Uh, myself, um, I'm a gamer, um, I do like to golf, I do like to, um, exercise, uh, something interesting also is, um, I met my, uh, wife here at the company who I still work with today.

Kovid Batra: Great. Thank you so much, Rich. Bryan, over to you. 

Bryan Finster: Oh, yes. I'm Bryan Finster. I've been a software developer for, oh, well, since 1996. So I'll let you do the math. I'm mostly doing enterprise development. I worked for Walmart for 19 of those years, um, in logistics for most of that time and, uh, helped pilot continuous delivery at Walmart inside logistics. I've got scars to show for it. Um, and then later moved to platform at Walmart, where I was originally in charge of the delivery metrics we were gathering to help teams understand how to do continuous delivery so they can compare themselves to what good continuous delivery looked like. And then later was asked to start a dojo at Walmart to directly pair with teams to help them solve the problem of how do we do CD. And then about a little over three years ago, I was, I joined Defense Unicorns as employee number three of three, uh, and we're, we're now, um, over 150 people. We're focused on how do we help the Department of Defense deliver, um, you know, do continuous delivery and secure environments. So it's a fun path.

Kovid Batra: Great, great. Perfect. And the same question to you. Something that LinkedIn doesn't tell about you, you would like to share with the audience. 

Bryan Finster: Um, computers aren't my hobby. Uh, I, you know, it's a lot better than roofing. My dad had a construction company, so I know what that's like. Um, but I, I very much enjoy photography, uh, collecting watches, ride motorcycles, and build plastic models. So that's where I spend my time. 

Kovid Batra: Nice. Great to know that. All right. So now I think, uh, we are good to go and start with the main section of, of our webinar. So I think first, uh, let's, let's start with you, Bryan. Um, I think you have been a long-time advocate of value streams, continuous delivery, DORA metrics. You just briefly told us about how this journey started, but let's, let's deep dive a little bit more into this. Uh, tell us about how value stream management, continuous delivery, all this as a concept started appealing to you from the point of Walmart and then how it has evolved over time for you in your life.

Bryan Finster: Sure. Uh, no, at Walmart, um, continuous delivery was the answer to a problem. It wasn't, it was, we had a business problem, you know, our lead time for change in logistics was a year. We were delivering every quarter with massive explosions. Every time we piloted, I mean, it was really stressful. Um, any, anytime we did a big change of recorder, we had planned 24 by 7 support for at least a week and sometimes longer, um, And it was just a complete nightmare. And our SVP, instead of hiring in a bunch of consultants, cause we've been through a whole bunch of agile transformations over the years, asked the senior engineers in the area to figure out how we can deliver every two weeks. Now, if you can imagine these giant explosions happening every two weeks instead of every quarter, we didn't want that. And so we started digging in, how do we get that done? And my partner in crime bought a copy of continuous delivery. We started reading that book cover to cover, pulling out everything we could, uh, started building Jenkins pipelines with templates, so the teams didn't have to go and build their own pipeline. They can just extend the base template which was a pattern we took forward later. And, and, uh, we built a global platform. I started trying to figure out how do we actually do the workflow that enables continuous delivery. I mean, we weren't testing at all. Think how scary that is. Uh, other than, you know, handing it off to QA and say, "Hey, test this for us.

And so I had to really dig into how do we do continuous integration. And then that led into what's the communication problems that are stopping us from getting information so we can test before we commit code. Um, and then once you start doing that at the team level, what's preventing us from getting all the other information that we need outside the team? How do we get the connection? You know that, all the, all the roadblocks that are preventing us from doing continuous delivery, how do we fix those? Which kind of let me fall backwards in the value stream management because now you're looking at the broader value stream. It's beyond just what your team can do. Um, and so it's, uh, it's, it's been just a journey of solving that problem of how do we allow every team to independently deploy from any other team as frequently as they can. 

Kovid Batra: Great. And, and how do, uh, DORA metrics and engineering metrics, while you are implementing these projects, taking up these initiatives, play a role in it?

Bryan Finster: Well, so, you know, all this effort that we went on predated Accelerate coming out, but I was going to DevOps Enterprise Summit and learning as much as I could starting in 2015 and talking to people about how do we measure things, cause I was actually sent to DevOps Enterprise Summit the first time to figure out how do we measure if we're doing it well, and then started pulling together, you know, some metrics to show that we're progressing on this path to CD, you know, how frequently integrating code, how many defects are being generated over time, you know, and how, how often can individuals on the team deploy as like, you know, deploys per day per developer was a metric that Jim proposed back in 2015 as just a health metric. How are we doing? And then later in the, and when we started the dojo in platform at Walmart, we were using a metrics-based approach to help teams. Continuous delivery was the method we were using to improve engineering excellence in the organization. We, you know, we weren't doing any Agile frameworks. It was just, why can't we deliver change daily? Um, and early on when we started building the platform, the first tool was the CI tool. Second tool was how do we measure. And we brought in CapitalOne's Hygieia, and then we gamified delivery metrics so we can show teams with a star rating how they were doing on integration frequency, build time, build success rate, deploy frequency, you know, and code complexity, that sort of thing, to show them, you know, this is what good looks like, and here's where you are. That's it. Now, I learned a lot from that, and there's some things I would still game today, and some things I would absolutely not gamify. Um, but that's where I, you know, I spent a long time running that as the game master about how do we, how do we run the game to get teams to want to, want to move and have shown where to go.

And then later, Accelerate came out, and the big thing that Accelerate did was it validated everything we thought was true. All the experiences we had, because the reason I'm so passionate about it is that first, first experience with CD was such a morale improvement on the team that I, nobody ever wanted to work any other way, and when things later changed, they were forced to not work that way by new leadership, everyone who could left. And that's just the reality of it. And, but accelerate came out and said these exact things that we were seeing. And it wasn't just a one-off. It wasn't just this, you know, just localized to. What we were saying, it was everywhere.

Kovid Batra: Yeah, totally makes sense. I think, uh, it's been a burning topic now, and a lot of, uh, talks have been around it. In fact, like, these things are at team-level, system-level. In fact, uh, I'm, uh, the McKinsey article that came out, uh, talking about dev productivity also. So I, I have actually a question there. So, uh. 

Bryan Finster: Oh, I shouldn't have read the article. Yeah, go ahead. 

Kovid Batra: I mean, it's basically, it's basically talking about individual, uh, dev productivity, right? People say that it can be measured. So yeah. What's your take on that? 

Bryan Finster: That's, that's really dumb. If you want to absolutely kill outcomes, uh, focus on HR metrics instead of outcome metrics, you know. And, and so, I want to touch a little bit on the DORA metrics I think. You know, I've, having worked to apply those metrics on top of the metrics we're already using, there's some of them that are useful, but you have to understand those came from surveys, and there's some of them that are, that if you try to measure them directly, you won't get the results you want, you won't get useful data from measuring directly. Um, you know, and they don't tell you things are going well, they only tell you things are going poorly and you can't use those as your, your, the thing that tells you whether, whether you're delivering value well, you know? It's just something that you, cause you to ask questions about what might be going wrong or not, but it's not, it's not something you use like a dashboard. 

Kovid Batra: Makes sense. And I think, uh, the book that you have written, uh, 'How to Misuse and Abuse DORA Metrics', I think, let's, let's talk, talk about that a little bit. Like you have summarized a lot of things there, how DORA metrics should not be used, or Engineering metrics for that matter should not be used. So like, when do you think, how do you think teams should be using it? When do the teams actually feel the need of using these metrics and in which areas? 

Bryan Finster: Well, I think observability in general is something people don't pay enough attention to. And not just, you know, not just production observability, but how are we working as a team. And, and really what we're trying to do is you have to think of it first from what are we trying to do with product development. Um, a big mistake people make is assuming that their idea is correct, and all we have to do is build something according to spec, make sure it tests according to spec and deliver it when we're done. When fundamentally, the idea is probably wrong. And so the question is, how big of a chunk of wrong idea do I want to deliver to the end user and which money do I want to spend doing that? So what we're trying to do is we're trying to become much more efficient about how we make change so we can make smaller change at lower costs so that we can be more effective about delivering value and deliver less wrong stuff. And so what you're really trying to do is you're trying to measure the, the, the way that we work, the way we test, to find areas where we can improve that workflow, so that we can reduce the cost and increase the velocity, which we can deliver change. So we can deliver smaller units of work more frequently, get faster feedback and adjust our idea, right? And so if you're not, if you're just looking at, "Oh, we just need to deliver faster." But you're not looking at why do we want to deliver faster is to get faster feedback on the idea. And also from my perspective, after 20 years of carrying a pager, fix production very, very quickly and safely, I think those are both key things.

And so what we're trying to do with the metrics is we're trying to identify where those problems are. And so in the paper I wrote for IT revolution, which was about twice as long as they asked me for on, on how to misuse and abuse DORA metrics, I went into the details of how we apply those metrics in real life. At Walmart, when we were working with teams to help them improve and also, you know, using them on ourselves, I think if a team really wants to focus on improving, the first thing they should measure is how well they're doing at continuous integration, you know, how frequently are we integrating code, how long does it take us to finish whatever a unit of work is, and what's our, uh, how many defects we're generating, uh, over time as a trend. And measure trends and improve all those trends at the same time. 

Kovid Batra: How do we measure this piece where we are talking about measuring the continuous integration? 

Bryan Finster: So, as an average on the team, how frequently are we integrating code? And you really want to be at least daily, right? And that's integrated to the trunk, not to some develop branch. And then also, you know, people generally work on a task or a story or whatever it is. How long does it take to go from when we start that work until it's delivered? What's that time frame? And there's, there's other times within that we can measure and that was when we get into value stream mapping. We can talk about that later, but, uh, we want small units of work because you get higher quality information if you get smaller units work and you're more predictable on delivery of that unit of work, which takes a lot of pressure off, it eliminates story points. But then you also have to balance those with the quality of what we did, and you can't measure that quality until it's in production, because test to spec doesn't mean it's good. 'Fit for purpose' means the user finds it good. 

Kovid Batra: Right. Can you give us some examples of where you have seen implementing these metrics went completely south instead of working positively? Like how exactly were they abused and misused in a scenario? 

Bryan Finster: Yeah, every single time somebody builds a dashboard without really understanding what the problems you're trying to solve are, I see, I've seen lots of people over the years since Accelerate was published, building dashboards to sell, but they don't understand the core problem they're trying to solve. But also, you know, when you have management who reads a book and says, Oh, look, here's an end, you know, I helped cause this problems, which is why I work so hard to fix it by saying, "Hey, look at these four key metrics." Aren't you? You know, this, this can tell you some things, but then they start using them as goals instead of health indicators that are contextual to individual teams. And when you start saying, "Hey, all teams must have this, this level of delivery frequency." Well, maybe, but everybody has their own delivery context. You're not going to deliver to an air-gapped environment as frequently as you are to, you know, AWS, right? And so, you have to understand what it is you're actually trying to do. What, what decisions are you going to make with any metric? What questions are you trying to answer before you go and measure it? You have to define what the problem is before you try to measure that you're successful at correcting the problem. 

Kovid Batra: Right. Makes sense. There are challenges that I've seen in teams. Uh, so of course, Typo is getting implemented in various organizations here. What we have commonly come across is teams tend to start using it, but sometimes it happens that when there are certain indicators highlighted from those metrics, they're not sure of what to do next.

Bryan Finster: Right. 

Kovid Batra: So I'm sure you must. 

Bryan Finster: Well, but the reason why is because they didn't know why they were measuring it in the first place, right? And so, like I said, you know, DORA metrics in specific, they tell you something, but they're very much trailing metrics, which is why I point to CI because CI is really the, the CI workflow is really the engine that starts driving improvement. And then, you know, once you get better at that, you say, "Well, why can't I deliver today's work today?" And you start finding other things in the value stream that are broken, but then you have to identify, okay, well, We see this issue here with code review. We see this issue here. We have this handoff to another team downstream of development before we can deploy. How do we improve those? And how can we measure that we are improving? So you have to ask the question first. And then come up with the metrics that you're using to evaluate success. 

And so, people are saying, well, I don't know what to do with this number. It's because they don't, they didn't, they started with a metric and then tried to figure out what to do with it because someone told him it was a good metric. No metric is a good metric unless you know what you're doing with it. I mean, if I put a tachometer on a car and you think that more is better but you don't understand what the tachometer is telling you, then you'll just blow up your engine. 

Kovid Batra: But don't you think like there is a possible way to actually not know what to measure, but to identify what to measure also from these metrics itself? For example, like, uh, we have certain benchmarks for, uh, different industries for each metric, right? And let's say I start looking at the lead time, I start looking at the deployment frequency, mean time to restore, there are various other metrics. And from there, I try to identify where my engineering efficiency or productivity is, productivity is getting impacted. So can, can it not be a top-down approach where we find out what we need to actually measure and improve upon from those metrics itself? 

Bryan Finster: Only if you start with a question you're trying to answer. But I wouldn't compare. So one of the other problems I have with the DORA metrics specifically is that the, and I've talked to DORA at Google about this as well, it's, it's like some of the questions are nonspecific. So for your, the system you work on most of the time, how frequently you deliver. Well, are you talking about a thousand developers, a hundred developers, a team of eight, right? And so, your delivery frequency is going to be very much relative to the number of people working on it, plus other constraints outside of it. And so you, yes, high performers deliver, you know, multiple times a day with, uh, you know, lead times of less than an hour, except that what's the definition of lead time? Well, there's two inside Accelerate, and they're different depending on how you read it. And, but that doesn't mean that you should just copy what it says. You should look at that and say, "Okay, now what, what am I trying to accomplish? And how can I apply these ideas? Not necessarily the metrics directly, but how can I apply these ideas to measure what I'm trying to measure to find out where my problems are?" So you have to deep dive into where your problems are. And so just like, "Hey, measure these things and here's your benchmarks.

Kovid Batra: Makes sense. Makes sense. Richard, do you have a point that I think we have been talking for a long, if you have any question, uh, I think let's, let's hear from Richard also. Um, he has used Typo, uh, has been using it for a while now, and I'm sure, uh, in this journey of implementing engineering metrics, DORA metrics in his team, he would have seen certain challenges. Richard, I think the stage is yours. 

Richard Pangborn: Yeah, sure. Um, so my research into using DORA metrics stem from, um, building high-performing teams. So, um, we always, we're looking for continuous improvement, but we're really looking for ways to measure ourselves that, that makes sense, that can't be totally gamed, that, um, that are like standards. Uh, what I liked about DORA was it had some counterbalancing metrics like throughput versus quality, time to repair versus time to build, speed for stability. That's, it's a, it's a nice counterbalancing, um, effect. Um, and high-performing teams, they care about stuff like continuous improvement, they want to do better than they did last quarter or, or last month, they want to, um, they want help with decision-making. So better data to drive some of the guesswork about, you know, what, what area needs, um, The most improvement or what area is, uh, broken in our pipeline, maybe for like continuous delivery for quality. Um, I want to make sure that they're making a difference, that they're moving a needle, um, ladders up. So a lot of times, a lot of companies, uh, have different measurements at different levels, like company level, department level, team level, individual level. So DORA, we were able to identify some that do ladder up, which is great. 

Some of the there are some challenges with implementing DORA, like when we first started. Um, so I think part of the challenge, one of the first ones was the complexity around data collection. Um, so, you know, accurately tracking and measuring DORA metrics. So deployment frequency, lead time for changes, failure rate, recovery, um, they all come from different sources. So CI/CD pipelines, version control systems, incident management tools. So integrating these data sources and ensuring they provide consistent results can be a little time consuming. Um, it can be a little difficult to understand. Yeah, so that was, that was definitely one part of it. Uh, we haven't rolled out all four yet. We're still in the process, just ensuring that, you know, what we are measuring is accurate.

Bryan Finster: Yeah, and I'm glad you touched on the accuracy thing. Um, When we would go and work with teams and start collecting data, so number one, we had data from the pipeline because it was embedded into the platform, but we also knew that when we worked with teams that the Git data was accurate, but the workload was going to be garbage unless the teams actually cared about using Jira correctly. And so, education step number one was while we were cleaning up the, the data in Jira, educating them on why Jira actually should matter to them, instead of just as a, it's not, it's not a time-tracking tool, it's a communication tool. You know, and educating them so that they would actually take it seriously so that the workflow data would be accurate so that they could then use that to help them identify where the improvements could happen because we're going to try to teach them how to improve, we weren't just trying to teach them to do what we said. Um, and yeah, I built a few data collection tools since we started this, and yeah, the collecting the data and showing where, um, accuracy problems happen as part of the dashboard is something that needs to be understood because people will just say, "Oh, the data's right." But yeah, I mean, especially with workflow data, one of the things we really did on the last one I built was show where, where the, you know, where we're out of bounds, very high or very low, you know. I talked to management. I was like, "Well, look, we're doing really good. I've got stuff closing here really fast." I'm like, you're telling me it took 30 seconds to do that, give it a work. Yeah, the accuracy issues. And MTTR is something that DORA's talked about ditching entirely because it's a far too noisy metric if you're trying to collect it automatically. 

Richard Pangborn: Yeah, we haven't started tracking MTTR yet. Um, we're more concerned with the throughput versus stability that would have the biggest, um, change at the department level, at the team level. Um, I think, I think that's made the difference so far. Also, we have a challenge with, um, yeah, just doing a lot of stuff manually. So lack of tooling and automation. Um, there's a lot of manual measurements that are taking place. So like you said, error-prone for data collection, inconsistent processes. Um, once we get to a more automated state, I feel like it will be a bit more successful.

Bryan Finster: Yeah. There's a dashboard I built for the, for the Air Force. I'll send you a link later. It might, it might be useful, I'm not sure. But also the other thing is change failure rate is something that people misunderstand a lot, uh, and I've, I've combed through Accelerate multiple times. Uh, uh, Walmart has actually asked to reverse engineer the survey for the book, so I've gone back in depth. Change failure rate is any defect. It's not an incident. If you go and read what it says about change failure rate, it's any defect, which it should be because also the idea is wrong. If the user's reporting it's defective, and you say, "Well, that's a new feature." No, the idea was defective. We're not, it's not fit for purpose in most, you know, unless it's some edge case, but we should track that as well, because that's part of our quality process and change failure rate's trying to track our quality process. 

Richard Pangborn: Another problem we had is, um, mean, uh, meantime to recovery. So because we track our bugs or defects differently, they have different priorities. So, um, P0s here has to be done, has to be fixed in less than 24 hours. Um, P, priority 1 means, you know, five days, priority two, you have two weeks. So trying to come up with a, an algorithm to accurately identify, um, time to fix, I guess you'd have like three, three or four different ones instead of one. 

Bryan Finster: I've tried to solve that problem too, and especially on distributed systems, it becomes very difficult. So who's getting measured on MTTR? I mean, I'm sorry. Yes, yes. Who's getting measured, right? It's going to be because MTTR, by definition, is when the user sees impact. And so really, that's whoever has the user interface owns that metric. If you're trying to help a team improve their processes for recovery. So it's, it's, it's just a really difficult metric to try to do anything with unless, um, well, you can't, it's, I've, I've, I've tried to measure it directly. I've talked to Verizon, CapitalOne, uh, you know, other people in the dojo consortium, they've tried to make, nobody's been successful at measuring it. But yeah. I think better metrics are out there for how fast we can resolve defects. 

Richard Pangborn: Um, one of the things we were concerned about at the beginning was like a resistance to measurement. Um, some people don't want to be measured. 

Bryan Finster: That's because they have management meeting over the head and using it as, as the reason why it's a massive fear thing. And it's part of the, it's a cultural thing. I mean, as long as you, it's, you have to have a generative culture to make these metrics effective. One of the things we would do when we start working with teams is number one, we'd explain to them, we're not trying to judge you. We're like your doctor. We're working with you. We're in the trenches with you. These are all of our metrics. They're not yours. And here's how to use them to help you improve. And if a manager comes and starts trying to beat you up with them, just, you know, stop making the data valid. 

Richard Pangborn: Yeah. Well, some developers do want to know am I doing well, how do I measure myself? Um, So this gives them a way to do it a little bit, but we told them, um, you know, you set your own goals. Improve yourself. Don't measure yourself next to a developer, another developer on your team or, or someone else where you're looking for your own improvement. 

Bryan Finster: Well, I think it's also really important that the smallest unit that's measured with delivery metrics is team and not person. If, if, if individuals are being measured, they're going to optimize for themselves instead of optimizing for team goals. And this is something I've seen, uh, frequently, uh, there was one, uh, with, you know, on, on our, on the dojo team, we can walk into your team and see that if there was filters by individual developer, your team was seriously broken. Uh, and I've seen managers who measured team members by how many Jira issues they closed, which meant that code review is going to be delayed, uh, mentoring was not going to happen, um, uh, you'd have senior engineers focusing on easy tasks to get their numbers up instead of focusing on solving the hard problems, design was not going to happen well because it wasn't a ticket, you know, and so you focus on team outcomes and measure team goals and individual performance because everybody has different roles on the teams. People know that from an HR perspective, coaching by walking around is how you find out who's struggling. You go to the gimbal, you find out who's struggling, you can't measure people directly, that way it'll impact team goals, business goals. 

Richard Pangborn: Yeah, I don't think we measure it as a, um, whether they're not successful, it's just something for them to, to watch themselves.

Bryan Finster: As long as somebody else can see it. I mean. 

Richard Pangborn: Yeah, it's just for them, isn't it? Not for anyone else. 

Bryan Finster: Yeah. 

Richard Pangborn: Um, cool. Yeah. Yeah. That's, that's about it for me. I think at the moment. 

Kovid Batra: Perfect, perfect. I think, uh, Rich, if, if you are done with your questions, we have already started seeing questions from the audience. 

Bryan Finster: There's one other thing I'd like to mention real quick before we go there.

Kovid Batra: Sure. 

Bryan Finster: I also gave a talk about how to misuse and abuse DORA metrics, and the fact that people think there's, yes, there's four key metrics they focus on, but read Accelerate. There's a lot more in that book for things that you should measure, including culture. Uh, it's, it's important that you look at this as a holistic thing and not just focus on these metrics to show how well we're doing at CD. Cool, but the most valuable thing in Accelerate is Appendix A and not the four key metrics. So that's number one. But number two, value stream maps, they're manual, but they give you far deeper insights into what's going wrong than the 4 key metrics will. So learn how to do value stream maps and learn how to use them to identify problems and fix those problems.

Kovid Batra: And how exactly, uh, so just an example, I'm expecting an example here, like when, when you are dealing with value stream maps, you're collecting data from system, you're collecting data from people through surveys and what exactly are you creating here? 

Bryan Finster: No, I don't collect any data from the system initially. So if I'm doing a value stream map, it'll be bringing a team together. We're not doing it at the, at the organization level. We're doing it at the team level. So you bring a team together and then you talk about the process, starting from delivery and working backwards to initiation of how we deliver change. Uh, you get a consensus from the team about how long things take, how long things are waiting to start. And then you start seeing things like, Oh, we do asynchronous code review, and so I'm ready for code review to start. Four to eight hours later, somebody picks it up and they review it. And then I find out later that they've done and there's changes being made, you know, maybe the next day. And then I go make those changes, resubmit it, and like four to eight hours later, somebody would go re-review it. And, and you see things like, Oh, well, what if we just sat down and discuss the change together and just fix it on the fly, um, and remove all that wait time? How much, you know, that would encourage smaller pieces of work? And we can deliver more frequently and get faster feedback and see, you can see just immediate improvements from things like that, just by doing a value stream map. But bringing the team together will give you much higher quality data than trying to instrument that because not all of those things are, there's data being collected anywhere.

Kovid Batra: Makes sense. All right. We'll take a minute break and we'll start with the Q and A after that. So audience, uh, please shoot out all your questions that you have.

All right. Uh, we have the first question. 

Bryan Finster: Yeah. So MTTR is a metric measuring customer impact. So the moment from when a customer is impacted or user impact until they are no longer impacted. And that doesn't mean you fix the defect. It means that you are no, they are no longer being impacted. So roll back, roll forward, doesn't matter. That's what MTTR has mentioned. 

Kovid Batra: Perfect. Let's, let's move on to the next one. 

Bryan Finster: Yeah. So, um, there's some things where I can set hard targets on as, as ways to know that we're doing well. Integration frequency is one of those, you know, if, if we're integrating once per day or better into the trunk, then we're doing a really good job of breaking down our work. We're doing a good job of testing, or as long as we keep our defects from blowing up, you know, we should be testing. But you can set targets for that. You can also set targets as a team, not something you impose on a team. This is something we as a team do that we want to keep a story size of two days or less. Paul Hammett would say one day or less. Uh, but I think two days is, is a good time limit, that if we, if it takes us more than two days, we'll start running into other dysfunctions that cause quality impact and, and issues with delivery. So I've built dashboards where I have a line on those two graphs that say "this is what good looks like", so the teams can compare themselves to good. Other things that you don't want to gamify, you don't ever want to measure test coverage and say, "Hey, this is what good test coverage looks like." Because test coverage doesn't measure quality. It just measures how much code is executed by code that says it's a test whether it's a test or not. So don't want to do that. That's a fail. I learned that the hard way. Delivery frequency, of course, it's, that's relative to their delivery problem. Uh, you may be delivering every day, every hour, every week, and that all could be good. It just depends. Um, but you can make objective measurements on integration frequency and how long a unit of work takes to do. 

Kovid Batra: Cool. Moving on to the next one. Uh, any recommendations where you learn, uh, where we can learn value stream maps? 

Bryan Finster: Yeah, so Steve Pereira and Andrew Davis released 'Flow Engineering', which is basically, because there's lots of books on value stream mapping, but it's, from the past, but they're mostly focused on manufacturing and Steve and Andrew released the Flow Engineering book where they talk about using value stream maps to identify problems and how to go about fixing those things. So it was just released earlier this year. 

Kovid Batra: Cool. Moving on to the next one. When would you start and how to convince upper management? They want KPI now and we are trying to get a VSM expert to come in and help. It's a hard sell. 

Bryan Finster: Yeah, yeah. We want easy numbers. Okay. Well, you know, I would, I would start with having a conversation about what problems we're trying to solve. It's very much like the conversation you have when you're trying to convince management that we want to do continuous delivery. They don't care about continuous delivery unless that they're, they're deep into the topic. But they do care about, uh, you know, delivering better about business value. So you talk about the business value. When you're talking about performance indicators, well, what performance are we trying to measure? And we really need to have that hard conversation about, are we trying to measure how much, how many lines of code are getting dumped onto the end user? How much value are we delivering? Are we trying to, you know, reduce the size and cost of delivering change so we can be more effective about this, or are we just trying to make sure people are busy? And so if you have management that just wants to make sure people are productive, uh, and they're not opening to listening to why they're wrong, I'd quit.

Kovid Batra: All right. Can we move on to the next one then?

Bryan Finster: Where's the next one? 

Kovid Batra: Yeah. 

Bryan Finster: Oh, okay. 

Kovid Batra: Is there any scientific evidence we can use to point out that working on small steps iteratively is better than working in larger batches? The goal is to avoid anecdotal evidence while discussing what can improve the development process. 

Bryan Finster: You know, the hard thing about software, uh, in an industry is that people don't like sharing their information, uh, the real information because it can be stock impacting. And so we're, we're going to get a scientific study from a private company. Um, but we have a, you know, a few centuries worth of, of knowledge about knowing that if you build a whole bunch of the wrong thing, that you're not going to sell it. Um, there's, you don't have to do a scientific study because we have knowledge from manufacturing. Uh, you know, the, the, the Simpsons, the documentary The Simpsons, where they talk about the Homer car, where they build the entirely wrong car and put the company out of business without, because there was no feedback loop on that car at all until it was unveiled. Right? That's, that's really the problem. We're doing product development. And if you go off and say, I have this brilliant, well, you know, like, uh, uh, what was the, uh, Silicon Valley, they spent so much money building something nobody wanted and they kept iterating and trying to find the right thing, but they kept building the complete thing and building the wrong thing and just burning money. And this, this is the problem we're trying to solve. And so you're, you're trying to get faster feedback about when we're wrong, because we're inventing something new. Edison didn't build a million wrong light bulbs and see if any, I see if they worked.

Kovid Batra: All right. I think we can move on to the next one. Uh, what strategies do you recommend for setting realistic yet ambitious goals based on our current DORA metrics? 

Bryan Finster: Uh, I would start with why can't we deliver today's work today? Well, I'd do that right after why can't we integrate today's work today? And then start finding out what those problems are and solving them. Uh, as far as ambitious goals, I mean, I think it's ambitious to be doing continuous delivery. Why can't we do continuous delivery? Uh, you know, one of the reasons why we put minimumcd. org together several years ago was because it's a list of problems to solve, and if you solve those problems, you can't solve those problems with an organization that's not a great place to work. You just can't. And the goal is to make it a better place to work. So solve those problems. That's an ambitious goal. Do CD. 

Kovid Batra: Richard, do you have a question? 

Richard Pangborn: Uh, myself? No? 

Kovid Batra: Yup. 

Richard Pangborn: Nope. 

Kovid Batra: Okay. One last one we'll take here. Uh, yeah. 

Bryan Finster: Yeah, so common pitfalls, and I think we touched on some of these before, is trying to instrument all but two of them. You could instrument two of them mostly, I think that, uh, you know, and change fail rate is not named well because of the description. It's really defect arrival rate. But even then, that depends on being able to collect data from defects and whether or not that's being collected in a disciplined manner. Um, delivery frequency, you know, people frequently measure that at the organization level, but that doesn't really tell you anything. You really need to get down to where the work is happening and try to measure that there. But then setting targets around delivery frequency, instead of identifying how do we improve, right? And it's, it's, it's all it is, is how do we, how do we get better, um, using them as goals? They're absolutely not goals. They're health indicators. You know, like I talked about the tachometer before, I don't have a goal of, we're going to run at 5, 000 RPM. I mean, number one, it depends on the engine, right? I mean, that would be really terrible for a sport bike, would blow up a diesel. So we, we need to, using them naively without understanding what they mean and what it is we're trying to do. I see it constantly. Uh, I and others who were early adopters of these met out, screaming about this for several years, and that's why I'm on here today is please, please don't use them incorrectly because it just hurts things.

Kovid Batra: Perfect. Uh, Bryan, I have one question. Uh, uh, like when, when teams are setting these benchmarks for different metrics that they have identified to be measured, what should be the ideal strategy, ideal way of setting those benchmarks? Because that's a question I get asked a lot. 

Bryan Finster: Let's say, they were never benchmarks in Accelerate either. What they said was is that we're seeing a correlation between companies with these outcomes and metrics that look like this. So those aren't industry benchmarks, that's a correlation they're making. And correlation is not equal causation. I will tell you that being really good at continuous delivery means that you can, if you have good ideas, deliver good ideas well, but being good at CD doesn't mean you're going to be good at, at, at, you know, meeting your business goals because it depends, you know, garbage in, garbage out. Um, and so, you don't set them as benchmarks. They're not benchmarks. They're health indicators. Use them as health indicators. How do we make this better? Use them as, as things to cause you to ask questions. Why can't we deliver more than once a month? 

Kovid Batra: So basically, if we are, let's say, for a lack of a better term, we use 'benchmarks'. There should, those should be set on the basis of the cadence of our own team, how they are working, how they are designed to deliver. That's how we should be doing. Is that what you mean? 

Bryan Finster: No, I would absolutely use them as health indicators, you know, track trends. Are we trending up? Are we trending down? And then use that as the basis of starting an investigation into why are we trending up? Why are we trending down? I mean, are we trending up because people think it's a goal? And were there some other metric that's going south that we're not aware of while we're, while we're focusing on this one thing getting better? I mean, this is Richard, I mean, you pointed out exactly. It's a good balance set of metrics if they're measured correctly unlike if it's collected correctly. And you can't, you know, another problem I see is people focusing on 1. I remember a director telling his area, "Hey, we're going to start using DORA metrics. But for change management purposes, we're only going to start by focusing on MTTR instead of anything else." They're a set, they go together, you know? You can't just peel one out. Um, so. 

Kovid Batra: Got it, got it. Yeah, that absolutely answers my question. All right. I think with that, we come to the end of this session. Uh, before we part, uh, any parting advice from you, Bryan, Rich? 

Richard Pangborn: Um, just what we found successful in our own journey. Every, every company is different. They all have their own different processes, their own way of doing things, their own way of building things. So, there's not exactly one right way to do it. It's usually by trial and error for each, probably each company, uh, I would say. Depending on the tooling that you want to choose, the way you want to break down tasks and deliver stories. Like for us, we chose one day tasks in Jira. Um, we didn't choose, uh, long-lived branches. Um, we're not trunk-based explicitly, but we're, our PRs last no longer than a day. Um, so this is what we find works well for us. We're delivering daily. We haven't gotten yet to the, um, you know, delivering multiple times a day, but that's, that's somewhere in the future that we're going to get to, but you have to balance that with business goals. You need to get buy-in from stakeholders before you can get, um, development time to sort of build out that, that structure. So, um, it's a process. Um, everyone's different. Um, but I think bringing in some of these KPIs or, or sorry, benchmarks or health metrics, whatever you want to call them, um, has worked for us in the way where we have more observability into how we operate as engineers than we've ever had in the past. Um, so it's been pretty beneficial for us. 

Bryan Finster: Yeah. I'd say that the observability is critical. Um, you know, I've, I've built a few dashboards for showing these things. And for people, for development teams who were, uh, focusing on "we want to improve", they always found value in those things. Um, but I, one, one caution I have is that if you are showing metrics on a dashboard, understand that the user experience of that will change people's behaviors. It's so important people understand. And whenever I'm building a dashboard, I'm showing offsetting metrics together in a way that they can't be separated, um, because you, otherwise you'll just focus on one. I want you to focus on those offsetting metrics as a group, make them all better. Um, but it only matters if people are looking at it. And if it's not a constant topic of conversation, um, it, it, it won't help at all. And I know, uh, Abi Noda and I have a difference of opinion on how, on data collection. You know, I'm big on, I want real-time data because I'm trying to improve quickly. Uh, he's big on surveys, but for me, and I don't get feedback fast enough on, um, with a survey to be able to correct the course correctly if I'm trying to do, if I'm trying to improve CI and CD. It's good for other stuff. Good for culture. So that's the difference. Um, but make sure that you're not just going out and buying a tool to measure these things that shows data in a way or has, you know, that causes bad behavior, um, or shows, or collects data in a way where it's not collecting it correctly. Really understand what you're doing before you go and implement a tool. 

Kovid Batra: Cool. Thanks for that piece of advice, Bryan, Rich. Uh, with that, I think that's our time. Just a quick announcement about the next webinar session, which is with the pioneer of CD, the co-author of the book 'Continuous Delivery', Dave Farley. That will be on 25th of September. So audience, stay tuned. I'll be sharing the link with you guys, sending you emails. Thank you so much. That's it for today. 

Bryan Finster: Thanks so much. 

Richard Pangborn: I appreciate it. 

Kovid Batra: Thanks, Rich. Thanks, Bryan.

Top 6 Jellyfish Alternatives

Software engineering teams are important assets for the organization. They build high-quality products, gather and analyze requirements, design system architecture and components, and write clean, efficient code. Measuring their success and identifying the potential challenges they may be facing is important. However, this isn’t always easy and takes a lot of time. 

And that’s how Engineering Analytics Tools comes to the rescue. One of the popular tools is Jellyfish which is widely used by engineering leaders and CTOs across the globe. 

While this is usually the best choice for the organizations, there might be chances that it doesn’t work for you. Worry not! We’ve curated the top 6 Jellyfish alternatives that you can consider when choosing an engineering analytics tool for your company.

What is Jellyfish? 

Jellyfish is a popular engineering management platform that offers real-time visibility into engineering organization and team progress. It translates tech data into information that the business side can understand and offers multiple perspectives on resource allocation. It also shows the status of every pull request and commits on the team. Jellyfish can be integrated with third-party tools such as Bitbucket, Github, Gitlab, JIRA, and other popular HR, Calendar, and Roadmap tools. 

However, its UI can be tricky initially and has a steep learning curve due to the vast amount of data it provides, which can be overwhelming for new users. 

Top Jellyfish Alternatives 

Typo 

Typo is another Jellyfish alternative that maximizes the business value of software delivery by offering features that improve SDLC visibility, developer insights, and workflow automation. It provides comprehensive insights into the deployment process through key DORA and other engineering metrics and offers engineering benchmarks to compare the team’s results across industries. Its automated code tool helps development teams identify code issues and auto-fix them before merging to master. It captures a 360-degree view of developers’ experience and includes an effective sprint analysis that tracks and analyzes the team’s progress. Typo can be integrated with tech tools such as GitHub, GitLab, Jira, Linear, and Jenkins. 

Price

  • Free: $0/dev/month
  • Starter: $16/dev/month
  • Pro: $24/dev/month
  • Enterprise: Quotation on request

LinearB 

LinearB is another leading software engineering intelligence platform that provides insights for identifying bottlenecks and streamlining software development workflow. It highlights automatable tasks to save time and enhance developer productivity. It also tracks DORA metrics and collects data from other tools to provide a holistic view of performance. Its project delivery tracker reflects project delivery status updates using planning accuracy and delivery reports. LinearB can be integrated with third-party applications such as Jira, Slack, and Shortcut. 

Price

  • Free: $0/dev/month
  • Business: $49/dev/month
  • Enterprise: Quotation on request

Waydev

Waydev is a software development analytics platform that provides actionable insights on metrics related to bug fixes, velocity, and more. It uses the agile method for tracking output during the development process and allows engineering leaders to see data from different perspectives. It emphasizes market-based metrics and ROI, unlike other platforms. Its resource planning assistance feature allows for avoiding scope creep and offers an understanding of the cost and progress of deliverables and key initiatives. Waydev can be integrated with well-known tools such as Gitlab, Github, CircleCI, and AzureOPS.

Price

  • Quotation on request

Pluralsight Flow 

Pluralsight Flow is a popular tool that tracks DORA metrics and helps to benchmark DevOps practices. It aggregates GIT data into comprehensive insights and offers a bird-eye view of what’s happening in development teams. Its sprint feature helps to make better plans and dive into the team’s accomplished work and whether they are committed or unplanned. Its team-level ticket filters, GIT tags, and other lightweight signals streamline pulling data from different sources. Pluralsight Flow can be integrated with manual and automated testing tools such as Azure DevOps, and GitLab.

Price

  • Core: $38/mo
  • Plus: $50/mo

Code Climate Velocity

Code Climate Velocity is a popular tool that uses repos to synthesize data and offers visibility into code coverage, coding practices, and security risks. It tracks issues in real time to help quickly move through existing workflows and allow engineering leaders to compile data on dev velocity and code quality. It has JIRA and GIT support that compresses into real-time analytics. Its customized dashboard and trends provide a view into each individual’s day-to-day tasks to long progress. Code Climate Velocity also provides technical debt assessment and style check in every pull request.

Price

  • Open Source: $0 (Free forever)
  • Startup: $0: Up to 4 seats
  • Team: $16.67/month/seat billed annually ($20 billed monthly)

Swarmia 

Swarmia is another well-known engineering effectiveness platform that provides quantitative insights into the software development pipeline. It offers visibility into three key areas: Business outcomes, developer productivity, and developer experience. It allows engineering leaders to create flexible and audit-ready software cost capitalization reports. It also identifies and fixes common teamwork antipatterns such as siloing and too much work in progress. Swarmia can be integrated with popular tools such as Slack, JIRA, Gitlab, Azure DevOps, and more. 

Price

  • Free: 0£/dev/month
  • Lite: 20£/dev/month
  • Standard: 39£/dev/month

Conclusion 

While we have shared top software development analytics tools, don’t forget to conduct thorough research before selecting for your engineering team. Check whether it aligns well with your requirements, facilitates team collaboration and continuous improvement, integrates seamlessly with your existing and upcoming tools, and so on. 

All the best! 

Cycle Time Breakdown: Minimizing PR Review Time

Cycle time is a critical metric that assesses the efficiency of your development process and captures the total time taken from the first commit to when the PR is merged or closed. 

PR Review Time is the third stage i.e. the time taken from the Pull Request creation until it gets merged or closed. Efficiently reducing PR Review is crucial for optimizing the development workflow. 

In this blog post, we'll explore strategies to effectively manage and reduce review time to boost your team's productivity and success.

What is Cycle Time?

Cycle time is a crucial metric that measures the average time PR spends in all stages of the development pipeline. These stages are: 

  • The Coding time represents the time taken to write and complete the code changes.
  • The Pickup time denotes the time spent before a pull request is assigned for review.
  • The Review time encompasses the time taken for peer review and feedback on the pull request.
  • The Merge time shows the duration from the approval of the pull request to its integration into the main codebase.

A shorter cycle time indicates an optimized process and highly efficient teams. This can be correlated with higher stability and enables the team to identify bottlenecks and quickly respond to issues with change. 

Why Measuring Cycle Time Matters? 

  • PR cycle time allows software development teams to understand how efficiently they are working. Low cycle time indicates a faster review process and quick integrations of code changes, leading to a high level of efficiency. 
  • Measuring cycle time helps to identify stages in the development process where work is getting stuck or delayed. This allows teams to pinpoint bottlenecks and areas that require attention. 
  • Monitoring PR Cycle Time regularly informs process improvements. Hence, helping teams create and implement more effective and streamlined workflows.
  • Cycle time fosters continuous improvement. This enables them to adapt to changing requirements more quickly, maintain a high level of productivity, and ship products faster. 
  • Cycle time allows better forecasting and planning which allows engineering teams to estimate project timelines regularly and manage stakeholder expectations.  

What is PR Review Time? 

The PR Review Time encompasses the time taken for peer review and feedback on the pull request. It is a critical component of PR Cycle Time that represents the duration of a Pull Request (PR) spent in the review stage before it is approved and merged. Review time is essential for understanding the efficiency of the code review process within a development team.

Conducting code reviews as frequently as possible is crucial for a team that strives for ongoing improvement. Ideally, code should be reviewed in near real-time, with a maximum time frame of 2 days for completion.

If your review time is high, the platform will display the review time as red - 

How to Identify High Review Time?

Long reviews can be identifed in the "Pull Request" tab and see all the open PRs.

You can also identify all the PRs having a high cycle time by clicking on view PRs in the cycle time card. 

See all the pending reviews in the “Pull Request” and identify them with the oldest review in sequence. 

Causes of High Review Time

Unawareness of the PR being issued

It's common for teams to experience communication breakdowns, even the most proficient ones. To address this issue, we suggest utilizing typo's Slack alerts to monitor requests that are left hanging. This feature allows channels to receive notifications only after a specific time period (12 hrs as default) has passed, which can be customized to your preference.

Another helpful practice is assigning a reviewer to work alongside developers, particularly those new to the team. Additionally, we encourage the team to utilize personal Slack alerts, which will directly notify them when they are assigned to review a code.

Large PRs

When a team is swamped with work, extensive pull requests may also be left unattended if reviewing them requires significant time. To avoid this issue, it's recommended to break down tasks into shorter and faster iterations. This approach not only reduces cycle time but also helps to accelerate the pickup time for code reviews.

Team is diverted to other work

When a bug is discovered that requires a patch to be made, a high-priority feature comes down from the CEO. In such situations, countless unexpected events may demand immediate attention, causing other ongoing work, including code reviews, to take a back seat.

Too much WIP

Code reviews are frequently deprioritized in favor of other tasks, such as creating pull requests with your own changes. This behavior is often a result of engineers misunderstanding how reviews fit into the broader software development lifecycle (SDLC). However, it's important to recognize that code waiting for review is essentially at the finish line, ready to be incorporated and provide value. Every hour that a review is delayed means one less hour of improvement that the new code could bring to the application.

Too few people are assigned to do reviews

Certain teams restrict the number of individuals who can conduct PR reviews, typically reserving this task for senior members. While this approach is well-intentioned and ensures that only top-tier code is released into production, it can create significant bottlenecks, with review requests accumulating on the desks of just one or a few people. This ultimately results in slower cycle times, even if it improves code quality.

Ways to Reduce Review Time

Here are some steps on how you can monitor and reduce your review time

Set Goals for the review time

With typo, you can set the goal to keep the review time under 24 hrs recommended by us. After setting the goal, the system sends personal Slack real-time alerts when PRs are assigned to be reviewed. 

Focus on high-priority items

Prioritize the critical functionalities and high-risk areas of the software during the review, as they are more likely to have significant issues. This can help you focus on the most critical items first and reduce review time.

Regular code reviews 

Conduct code reviews frequently to catch and fix issues early on in the development cycle. This ensures that issues are identified and resolved quickly, rather than waiting until the end of the development cycle.

Create standards and guidelines 

Establish coding standards and guidelines to ensure consistency in the codebase, which can help to identify potential issues more efficiently. Keep a close tab on the following metrics that can impact your review time  - 

  • PR merged w/o review
  • Pickup time
  • PR size

Effective communication 

Ensure that there is clear communication among the development team and stakeholders to quickly identify issues and resolve them timely. 

Conduct peer reviews 

Peer reviews can help catch issues that may have been missed during individual code reviews. By having team members review each other's code, you can ensure that all issues are caught and resolved quickly.

Conclusion

Minimizing PR review time is crucial for enhancing the team's overall productivity and efficient development workflow. By implementing these, organizations can significantly reduce cycle times and enable faster delivery of high-quality code. Prioritizing these practices will lead to continuous improvement and greater success in software development process.

Become an Elite Team With Dora Metrics

In the world of software development, high performing teams are crucial for success. DORA (DevOps Research and Assessment) metrics provide a powerful framework to measure the performance of your DevOps team and identify areas for improvement. By focusing on these metrics, you can propel your team towards elite status.

What are DORA Metrics?

DORA metrics are a set of four key metrics that measure the efficiency and effectiveness of your software delivery process:

  • Deployment Frequency: This metric measures how often your team successfully releases new features or fixes to production.
  • Lead Time for Changes: This metric measures the average time it takes for a code change to go from commit to production.
  • Change Failure Rate: This metric measures the percentage of deployments that result in production incidents.
  • Mean Time to Restore (MTTR): This metric measures the average time it takes to recover from a production incident.

Why are DORA Metrics Important?

DORA metrics provide valuable insights into the health of your DevOps practices. By tracking these metrics over time, you can identify bottlenecks in your delivery process and implement targeted improvements. Research by DORA has shown that high-performing teams (elite teams) consistently outperform low-performing teams in all four metrics. Here's a quick comparison:

These statistics highlight the significant performance advantage that elite teams enjoy. By striving to achieve elite performance in your DORA metrics, you can unlock faster deployments, fewer errors, and quicker recovery times from incidents.

How to Achieve Elite Levels of DORA Metrics

Here are some key strategies to achieve elite levels of DORA metrics:

  • Embrace a Culture of Continuous Delivery:
    A culture of continuous delivery emphasizes automating the software delivery pipeline. This allows for faster and more frequent deployments with lower risk.
  • Invest in Automation:
    Automating manual tasks in your delivery pipeline can significantly reduce lead times and improve deployment frequency. This includes automating tasks such as testing, building, and deployment.
  • Break Down Silos:
    Effective collaboration between development, operations, and security teams is essential for high performance. Break down silos between these teams to foster a shared responsibility for delivery.
  • Implement Continuous Feedback Loops:
    Establish feedback loops throughout your delivery pipeline to identify and fix issues early. This can involve practices like code reviews, automated testing, and performance monitoring.
  • Focus on Error Prevention:
    Shift your focus from fixing errors in production to preventing them from occurring in the first place. Utilize tools and techniques like static code analysis and unit testing to catch errors early in the development process.
  • Measure and Monitor:
    Continuously track your DORA metrics to identify trends and measure progress. Use data-driven insights to guide your improvement efforts.
  • Promote a Culture of Learning:
    Create a culture of continuous learning within your team. Encourage team members to experiment with new technologies and approaches to improve delivery performance.

By implementing these strategies and focusing on continuous improvement, your DevOps team can achieve elite levels of DORA metrics and unlock significant performance gains. Remember, becoming an elite team is a journey, not a destination. By consistently working towards improvement, you can empower your team to deliver high-quality software faster and more reliably.

Additional Tips

In addition to the above strategies, here are some additional tips for achieving elite DORA metrics:

  • Set clear goals for your DORA metrics and track your progress over time.
  • Communicate your DORA metrics goals to your entire team and get everyone on board.
  • Celebrate successes and milestones along the way.
  • Continuously seek feedback from your team and stakeholders and adapt your approach as needed.

By following these tips and focusing on continuous improvement, you can help your DevOps team reach new heights of performance.

Leveraging LLM Models to Achieve DevOps Excellence

As you embark on your journey to DevOps excellence, consider the potential of Large Language Models (LLMs) to amplify your team's capabilities. These advanced AI models can significantly contribute to achieving elite DORA metrics.

Specific Use Cases for LLMs in DevOps

Code Generation and Review:

  • Autogenerate boilerplate code, unit tests, or even entire functions based on natural language descriptions.
  • Assist in code reviews by suggesting improvements, identifying potential issues, and enforcing coding standards.

Incident Response and Root Cause Analysis:

  • Analyze log files, error messages, and monitoring data to swiftly identify the root cause of incidents.
  • Generate incident reports and suggest remediation steps.

Documentation Generation:

  • Create and maintain up-to-date documentation for codebases, infrastructure, and processes.
  • Generate API documentation, user manuals, and knowledge bases.

Predictive Analytics:

  • Analyze historical data to forecast potential issues, such as infrastructure bottlenecks or application performance degradation.
  • Provide early warnings to prevent service disruptions.

Chatbots and Virtual Assistants:

  • Develop intelligent chatbots to provide support to developers and operations teams.
  • Automate routine tasks and answer frequently asked questions.

Natural Language Querying of DevOps Data:

  • Allow users to query DevOps metrics and data using natural language.
  • Generate insights and visualizations based on user queries.

Automation Scripting:

  • Assist in generating scripts for infrastructure provisioning, configuration management, and deployment automation.
  • Improve automation efficiency and reduce human error.

By strategically integrating LLMs into your DevOps practices, you can enhance collaboration, improve decision-making, and accelerate software delivery. Remember, while LLMs offer significant potential, human expertise and oversight remain crucial for ensuring accuracy and reliability.

Cycle Time Breakdown: Minimizing Coding Time

Cycle time is a critical metric for assessing the efficiency of your development process that captures the total time taken from the start to the completion of a task.

Coding time is the first stage i.e. the duration from the initial commit to the pull request submission. Efficiently managing and reducing coding time is crucial for maintaining swift development cycles and ensuring timely project deliveries.

Focusing on minimizing coding time can enhance their workflow efficiency, accelerate feedback loops, and ultimately deliver high-quality code more rapidly. In this blog post, we'll explore strategies to effectively manage and reduce coding time to boost your team's productivity and success.

What is Cycle Time?

Cycle time measures the total elapsed time taken to complete a specific task or work item from the beginning to the end of the process.

  • The Coding time represents the time taken to write and complete the code changes.
  • The Pickup time denotes the time spent before a pull request is assigned for review.
  • The Review time encompasses the time taken for peer review and feedback on the pull request.
  • The Merge time shows the duration from the approval of the pull request to its integration into the main codebase.

Longer cycle time leads to delayed project deliveries and hinders overall development efficiency. On the other hand, Short cycle time enables faster feedback, quicker adjustments, and more efficient development, leading to accelerated project deliveries and improved productivity.

Why Measuring Cycle Time Improves Engineering Efficiency? 

Measuring cycle time provides valuable insights into the efficiency of a software engineering team's development process. Below are some of how measuring cycle time can be used to improve engineering team efficiency:

  • Measuring cycle time for individual tasks or user stories can identify stages in the development process where work tends to get stuck or delayed. This helps to pinpoint bottlenecks and areas that need improvement.
  • Cycle time indicates the overall efficiency of your development process. Shorter cycle times generally reflect a streamlined and efficient workflow.
  • Understanding cycle time helps with better forecasting and planning. Knowing how long it typically takes to complete tasks can accurately estimate project timelines and manage stakeholder expectations.
  • Measuring cycle time allows you to evaluate the impact of process changes. 
  • Effective cycle time data for individual team members provides insights into their productivity and can be used for performance evaluations.
  • Tracking cycle time across multiple projects or teams allows process standardization and best practice identification.

What is Coding Time? 

Coding time is the time it takes from the first commit to a branch to the eventual submission of a pull request. It is a crucial part of the development process where developers write and refine their code based on the project requirements. High coding time can lead to prolonged development cycles, affecting delivery timelines. Managing the coding time efficiently is essential to ensure the code completion is done on time with quicker feedback loops and a frictionless development process. 

To achieve continuous improvement, it is essential to divide the work into smaller, more manageable portions. Our research indicates that on average, teams require 3-4 days to complete a coding task, whereas high-performing teams can complete the same task within a single day.

In the Typo platform, If your coding time is high, your main dashboard will display the coding time as red.

Screenshot 2024-03-16 at 1.14.10 AM.png

 

Screenshot 2024-05-12 at 12.22.04 AM.png

Benchmarking coding time helps teams identify areas where developers may be spending excessive time, allowing for targeted improvements in development processes and workflows. It also enables better resource allocation and project planning, leading to increased productivity and efficiency.

How to Identify High-Coding Time?

Identify the delay in the “Insights” section at the team level & sort the teams by the cycle time. 

Screenshot 2024-03-16 at 12.29.43 AM.png

Click on the team to deep dive into the cycle time breakdown of each team & see the delays in the coding time. 

Causes of High Coding Time

There are broadly three main causes of high coding time

  • The task is too large on its own
  • Task requirements need clarification
  • Too much work in progress

The Task is Too Large

Frequently, a lengthy coding time can suggest that the tasks or assignments are not being divided into more manageable segments. It would be advisable to investigate repositories that exhibit extended coding times for a considerable number of code changes. In instances where the size of a PR is substantial, collaborating with your team to split assignments into smaller, more easily accomplishable tasks would be a wise course of action.

“Commit small, commit often” 

Task Requirements Need clarification

While working on an issue, you may encounter situations where seemingly straightforward tasks unexpectedly grow in scope. This may arise due to the discovery of edge cases, unclear instructions, or new tasks added after the assignment. In such cases, it is advisable to seek clarification from the product team, even if it may take longer. Doing so will ensure that the task is appropriately scoped, thereby helping you complete it more effectively

There are occasions when a task can prove to be more challenging than initially expected. It could be due to a lack of complete comprehension of the problem, or it could be that several "unknown unknowns" emerged, causing the project to expand beyond its original scope. The unforeseen difficulties will inevitably increase the overall time required to complete the task.

Too Much Work in Progress

When a developer has too many ongoing projects, they are forced to frequently multitask and switch contexts. This can lead to a reduction in the amount of time they spend working on a particular branch or issue, increasing their coding time metric.

Use the work log to understand the dev’s commits over a timeline to different issues. If a developer makes sporadic contributions to various issues, it may be indicative of frequent context switching during a sprint. To mitigate this issue, it is advisable to balance and rebalance the assignment of issues evenly and encourage the team to avoid multitasking by focusing on one task at a time. This approach can help reduce coding time.

Screenshot 2024-03-16 at 12.52.05 AM.png

Ways to Prevent High Coding Time

Set up Slack Alerts for High-Risk Work

Set goals for the work at risk where the rule of thumb is keeping the PR with less than 100 code changes & refactor size as above 50%. 

To achieve the team goal of reducing coding time, real-time Slack alerts can be utilised to notify the team of work at risk when large and heavily revised PRs are published. By using these alerts, it is possible to identify and address issues, story-points, or branches that are too extensive in scope and require breaking down.

Balance Workload in the Team

To manage workloads and assignments effectively, it is recommended to develop a habit of regularly reviewing the Insights tab, and identifying long PRs on a weekly or even daily basis. Additionally, examining each team member's workload can provide valuable insights. By using this data collaboratively with the team, it becomes possible to allocate resources more effectively and manage workloads more efficiently.

Use a Framework

Using a framework, such as React or Angular, can help reduce coding time by providing pre-built components and libraries that can be easily integrated into the application

Code Reuse

Reusing code that has already been written can help reduce coding time by eliminating the need to write code from scratch. This can be achieved by using code libraries, modules, and templates.

Rapid Prototyping

Rapid prototyping involves creating a quick and simple version of the application to test its functionality and usability. This can help reduce coding time by allowing developers to quickly identify and address any issues with the application.

Use Agile Methodologies

Agile methodologies, such as Scrum and Kanban, emphasize continuous delivery and feedback, which can help reduce coding time by allowing developers to focus on delivering small, incremental improvements to the application

Pair Programming

Pair programming involves two developers working together on the same code at the same time. This can help reduce coding time by allowing developers to collaborate and share ideas, which can lead to faster problem-solving and more efficient coding.

Conclusion

Optimizing coding time, a key component of the overall cycle time enhances development efficiency and accelerates project delivery. By focusing on reducing coding time, software development teams can streamline their workflows and achieve quicker feedback loops. This leads to a more efficient development process and timely project completions. Implementing strategies such as dividing tasks into smaller segments, clarifying requirements, minimizing multitasking, and using effective tools and methodologies can significantly improve both coding time and cycle time.

Top 5 Waydev Alternatives

Software engineering teams are the engine that drives your product forward. They write clean, efficient code, gather and analyze requirements, design system architecture and components, and build high-quality products. And since the tech industry is ever-evolving, it is crucial to understand how well they are performing and what needs to be fixed. 

This is where software development analytics tools come in. These tools provide insights into various metrics related to the development workflow, measure progress, and help to make informed decisions.

One such tool is Waydev that is used by development teams across the globe. While this is usually the best choice for the organizations, there might be chances that it doesn’t work for you. 

We’ve curated the top 5 Waydev alternatives that you can consider when selecting engineering analytics tools for your company.

What is Waydev?

Waydev is a leading software development analytics platform that puts more emphasis on market-based metrics. It allows development teams to compare the ROI of specific products to identify which features need improvement or removal. It also gives insights into the cost and progress of deliverables and key initiatives. Waydev can be seamlessly integrated with Github, Gitlab, CircleCI, Azure DevOps, and other popular tools. 

However, this analytics tool can be expensive, particularly for smaller teams or startups and may lack certain functionalities, such as detailed insights into pull request statistics or ticket activity. 

Top Waydev Alternatives 

A few of the best Waydev alternatives are: 

Typo 

Typo is a software engineering analytics platform that offers SDLC visibility, actionable insights, and workflow automation for building high-performing software teams. It tracks essential DORA and other engineering metrics to assess their performance and improve DevOps practices. It allows engineering leaders to analyze sprints with detailed insights on tasks and scope and provides an AI-powered team insights summary. Typo’s built-in automated code analysis helps find real-time issues and hotspots across the code base to merge clean, secure, high-quality code, faster. With its holistic framework to capture developer experience, Typo helps understand how devs are doing and what can be done to improve their productivity. Its pre-built integration in the dev tool stack can highlight developer blockers, predict sprint delays, and measure business impact.

Price:

  • Free: $0/dev/month
  • Starter: $16/dev/month
  • Pro: $24/dev/month
  • Enterprise: Quotation on request

LinearB

LinearB is another software delivery intelligence platform that provides insights to help engineering teams identify bottlenecks and improve software development workflow. It highlights automatable tasks to save time and resources and enhance developer productivity. It provides real-time alerts to development teams regarding project risks, delays, and dependencies and allows teams to create customized dashboards for tracking various engineering metrics such as cycle time and DORA metrics. LinearB’s project delivery forecast alerts the team to stay on schedule and communicate project delivery status updates. It can also be integrated with third-party applications such as Jira, Slack, Shortcut, and other popular tools.‍ 

Price:

  • Free: $0/dev/month
  • Business: $49/dev/month
  • Enterprise: Quotation on request

Jellyfish 

Jellyfish is an engineering management platform that aligns engineering data with business priorities. provides real-time visibility into engineering work quickly and allows the team members to track key metrics such as PR statuses, code commits, and overall project progress. It can be integrated with various development tools such as GitHub, GitLab, JIRA, and other third-party applications. Jellyfish offers multiple perspectives on resource allocation and helps track investments made during product development. It also generates reports tailored for executives and finance teams, including insights into R&D capitalization and engineering efficiency. 

Price

  • Quotation on request

Swarmia 

Swarmia is an engineering effectiveness platform that provides visibility into three key areas: business outcome, developer productivity, and developer experience. Its working agreement feature includes 20+ work agreements, allowing teams to adopt and measure best practices from high-performing teams. It tracks healthy engineering measures and provides insights into the development pipeline. Swarmia’s Investment balance gives insights into the purpose of each action and money spent by the company on each category. It can be integrated with tech tools like source code hosting, issue trackers, and chat systems.

Price

  • Free: 0£/dev/month
  • Lite: 20£/dev/month
  • Standard: 39£/dev/month

Pluralsight Flow

Pluralsight Flow, a software development analytics platform, aggregates GIT data into comprehensive insights. It gathers important engineering metrics such as DORA metrics, code commits, and pull requests, all displayed in a centralized dashboard. It can be integrated with manual and automated testing such as Azure DevOps and GitLab. Pluralsight Flow offers a comprehensive view of team health, allowing engineering leaders to proactively diagnose issues. It also sends real-time alerts to keep teams informed about critical changes and updates in their workflows.

Price

  • Core: $38/mo
  • Plus: $50/mo

How to Select the Right Software Development Analytics Tool for your Team?

Picking the right analytics tool is important for the software engineering team. Check out these essential factors below before you make a purchase:

Scalability

Consider how the tool can accommodate the team’s growth and evolving needs. It should handle increasing data volumes and support additional users and projects.

Error Detection

Error detection feature must be present in the analytics tool as it helps to improve code maintainability, mean time to recovery, and bug rates.

Security Capability

Developer analytics tools must compile with industry standards and regulations regarding security vulnerabilities. It must provide strong control over open-source software and indicate the introduction of malicious code.

Ease of Use

These analytics tools must have user-friendly dashboards and an intuitive interface. They should be easy to navigate, configure, and customize according to your team’s preferences.

Integrations

Software development analytics tools must be seamlessly integrated with your tech tools stack such as CI/CD pipeline, version control system, issue tracking tools, etc.

Conclusion

Given above are a few Waydev competitors. Conduct thorough research before selecting the analytics tool for your engineering team. Check whether it aligns well with your requirements. It must enhance team performance, improve code quality and reduce technical debt, drive continuous improvement in your software delivery and development process, integrate seamlessly with third-party tools, and more.

All the best!

Top DevOps Metrics and KPIs (2024)

As an engineering leader, showcasing your team’s efficiency and alignment with business goals can be challenging. DevOps metrics and KPIs are essential tools that provide clear insights into your team’s performance and the effectiveness of your DevOps practices.

Tracking the right metrics allows you to measure the DevOps processes’ success, identify areas for improvement, and ensure that your software delivery meets high standards. 

In this blog post, let’s delve into key DevOps metrics and KPIs to monitor to optimize your DevOps efforts and enhance organizational performance.

What are DevOps Metrics and KPIs? 

DevOps metrics showcase the performance of the DevOps software development pipeline. These metrics bridge the gap between development and operations and measure and optimize the efficiency of processes and people involved. Tracking DevOps metrics enables DevOps teams to quickly identify and eliminate bottlenecks, streamline workflows, and ensure alignment with business objectives.

DevOps KPIs are specific, strategic metrics to measure progress towards key business goals. They assess how well DevOps practices align with and support organizational objectives. KPIs also provide insight into overall performance and help guide decision-making.

Why Measure DevOps Metrics and KPIs? 

Measuring DevOps metrics and KPIs is beneficial for various reasons:

  • DevOps Metrics help identify areas where processes may be inefficient or problematic. Hence, enabling teams to address issues and optimize performance.
  • Tracking metrics allows development teams to maintain high standards for software quality and reliability.
  • They provide a basis for evaluating the effectiveness of DevOps practices and making data-driven decisions to drive continuous improvement and enhance processes.
  • KPIs ensure that DevOps efforts are aligned with broader business objectives. This allows organizations to achieve strategic goals and deliver value to the end-users. 
  • They provide visibility into the DevOps process that fosters better communication and collaboration within DevOps teams. 
  • Measuring metrics continuously allows teams to monitor progress, set benchmarks, and assess the impact of changes and improvements.
  • They help make strategic decisions, allowing resource utilization effectively and prioritizing initiatives based on their impact.

Key DevOps Metrics and KPIs

There are many DevOps metrics available. Focus on the key performance indicators that align with your business needs and requirements. 

A few important DevOps metrics and KPIs are:

Deployment Frequency

Deployment Frequency measures how often the code is deployed to production. It considers everything from bug fixes and capability improvements to new features. It monitors the rate of change in software development, highlights potential issues, and is a key indicator of agility and efficiency. High deployment Frequency indicates regular deployments and a streamlined pipeline, allowing teams to deliver features and updates faster. 

Lead Time for Changes

Lead Time for Changes is a measure of time taken by code changes to move from inception to deployment. It tracks the speed and efficiency of software delivery and provides valuable insights into the effectiveness of development processes, deployment pipelines, and release strategies. Short lead times allow new features and improvements to reach users quickly and enable organizations to test new ideas and features. 

Change Failure Rate

This DevOps metric tracks the percentage of newly deployed changes that caused failure or glitches in production. It reflects reliability and efficiency and relates to team capacity, code complexity, and process efficiency, impacting speed and quality. Tracking CFR helps identify bottlenecks, flaws, or vulnerabilities in processes, tools, or infrastructure that can negatively affect the software delivery’s quality, speed, and cost. 

Mean Time to Recovery

Mean Time to Recovery measures the average time a system or application takes to recover from any failure or incident. It highlights the efficiency and effectiveness of an organization’s incident response and resolution procedures. Reduced MTTR minimizes system downtime, faster recovery from incidents, and identifies and addresses potential issues quickly. 

Cycle Time

Cycle Time metric measures the total elapsed time taken to complete a specific task or work item from the beginning to the end of the process. Measuring cycle time can provide valuable insights into the efficiency and effectiveness of an engineering team's development process. These insights can help assess how quickly the team can turn around tasks and features, identify trends and failures, and forecast how long future tasks will take.

Mean Time to Detection

Mean Time to Detection is a key performance indicator that tracks how long the DevOps team takes to identify issues or incidents. High time to detect results in bottlenecks that may interrupt the entire workflow. On the other hand, shorter MTTD indicates issues are identified rapidly, improving incident management strategies and enhancing overall service quality. 

Defect Escape Rate

Defect Escape Rate tracks how many issues slipped through the testing phase. It monitors how often defects are uncovered in the pre-production vs. production phase. It highlights the effectiveness of the testing and quality assurance process and guides improvements to improve software quality. Reduced Defect Escape Rate helps maintain customer trust and satisfaction by decreasing the bugs encountered in live environments. 

Code Coverage

Code coverage measures the percentage of a codebase tested by automated tests. It helps ensure that the tests cover a significant portion of the code, and identifies untested parts and potential bugs. It assists in meeting industry standards and compliance requirements by ensuring comprehensive test coverage and provides a safety net for the DevOps team when refactoring or updating code. Hence, they can quickly catch and address any issues introduced by changes to the codebase. 

Work in Progress

Work in Progress represents the percentage breakdown of Issue tickets or Story points in the selected sprint according to their current workflow status. It monitors and manages workflow within DevOps teams. It visualizes their workload, assesses performance, and identifies bottlenecks in the dev process. Work in Progress enables how much work the team handles at a given time and prevents them from being overwhelmed. 

Unplanned Work

Unplanned work tracks any unexpected interruptions or tasks that arise and prevents engineering teams from completing their scheduled work. It helps DevOps teams understand the impact of unplanned work on their productivity and overall workflow and assists in prioritizing tasks based on urgency and value.

Pull Request Size

PR Size tracks the average number of lines of code added and deleted across all merged pull requests (PRs) within a specified time period. Measuring PR size provides valuable insights into the development process, helps development teams identify bottlenecks, and streamline workflows. Breaking down work into smaller PRs encourages collaboration and knowledge sharing among the DevOps team. 

Error Rates

Error Rates measure the number of errors encountered in the platform. It identifies the stability, reliability, and user experience of the platform. Monitoring error rates help ensure that applications meet quality standards and function as intended otherwise it can lead to user frustration and dissatisfaction. 

Deployment Time

Deployment time measures how long it takes to deploy a release into a testing, development, or production environment. It allows teams to see where they can improve deployment and delivery methods. It enables the development team to identify bottlenecks in the deployment workflow, optimize deployment steps to improve speed and reliability, and achieve consistent deployment times. 

Uptime

Uptime measures the percentage of time a system, service, or device remains operational and available for use. A high uptime percentage indicates a stable and robust system. Constant uptime tracking maintains user trust and satisfaction and helps organizations identify and address issues quickly that may lead to downtime.

Improve Your DevOps KPIs with Typo

Typo is one of the effective DevOps tools that offer SDLC visibility, developer insights, and workflow automation to deliver high-quality software to end-users. It can seamlessly integrate into tech tool stacks such as GIT versioning, issue tracker, and CI/CD tools. It also offers comprehensive insights into the deployment process through key metrics such as change failure rate, PR size, code coverage, and deployment frequency. Its automated code review tool helps identify issues in the code and auto-fixes them before you merge to master.

  • Offers customized DORA metrics and other engineering metrics that can be configured in a single dashboard.
  • Analyze the code coverage report within a few minutes and provide detailed coverage reports.
  • Auto-analyses codebase and pull requests to find issues and auto-generates fixes before you merge to master. 
  • Offers engineering benchmark to compare the team’s results across industries.
  • User-friendly interface.

Conclusion

DevOps metrics are vital for optimizing DevOps performance, making data-driven decisions, and aligning with business goals. Measuring the right key indicators can gain insights into your team’s efficiency and effectiveness. Choose the metrics that best suit the organization’s needs, and use them to drive continuous improvement and achieve your DevOps objectives.

Webinar: ‘The Hows & Whats of DORA’ with Nathen Harvey and Ido Shveki

Typo recently hosted an engaging live webinar titled “The Hows and Whats of DORA,” featuring DORA expert Nathen Harvey and special guest Ido Shveki. With over 170 attendees, we explored DORA and other crucial engineering metrics in depth.

Nathen, the DORA Lead & Developer Advocate at Google Cloud, and Ido, the VP of R&D at BeeHero, one of our valued customers, brought their unique insights to the discussion.

The session explored why only 5-10% of engineering teams actively use DORA metrics and examined the current state of data-driven metrics like DORA and SPACE. It also highlighted the organizational and cultural elements essential for successfully implementing these metrics.

Further, Nathen explained how advanced frameworks such as DORA become critical based on team size and DevOps maturity, and offered practical guidance on choosing the most relevant metrics and benchmarks for the organization.

The event concluded with an engaging Q&A session, allowing attendees to ask questions and gain valuable insights.

P.S.: Our next live webinar is on August 28, featuring DORA expert Bryan Finster. We hope to see you there!

Want to Implement DORA Metrics for Improving Dev Visibility and Performance?

Timestamps

  • 00:00 - Introduction
  • 02:11 - Understanding the Low Uptake of Metrics
  • 08:11 - Mindset Shifts Essential for Metrics Implementation
  • 10:11 - Ideal Team Size for Metrics Implementation
  • 15:36 - How to Identify Benchmarks?
  • 22:06 - Aligning Business with Engineering Metrics
  • 25:04 - Choosing the Right Metrics
  • 30:49 - Q&A Session
  • 45:43 - Conclusion

Links and Mentions 

Webinar Transcript

Kovid Batra: All right. Hi, everyone. Thanks for joining in for our DORA Exclusive webinar- The Hows & Whats of DORA, powered by Typo. This is Kovid, founding member at Typo and your host for today's webinar. And with me, I have two special co-hosts. Please welcome the DORA expert tonight, Nathen Harvey. He's the Lead and Dev Advocate at Google Cloud. And we have with us one of our product mentors, Typo Advocates, Ido Shveki, who is VP of R&D at BeeHero. Thanks, Nathen. Thanks, Ido, for joining in. 

Nathen Harvey: Oh, thanks for having us. I'm really excited to be here today. Thanks, Kovid. 

Ido Shveki: Me too. Thanks, Kovid. 

Kovid Batra: Guys, um, honestly, like before we get started, uh, I have to share this with the, with our audience today. Uh, both of you have been really nice. It was just one message and you were so positive in the first response itself to join this event. And honestly, uh, I feel that this, these kinds of events are really helpful for the engineering community because we are picking up a topic which is growing, people want to learn more, and, uh, Nathen, Ido,, once again, thanks a lot for, for joining in on this event. 

Nathen Harvey: Oh yeah, it really is my pleasure and I totally agree that these events are so important. Um, I often say that, you know, you can't improve alone. Uh, and that's true that each individual, we can't improve our entire organization or even our entire team on our own. It requires the entire team, but even an entire team within one organization, there's so much that we can learn from each other when we look into other organizations around the world and other challenges that people are running into, how they've overcome them. And I truly believe that each and every one of us has something to share with the community, uh, even if you were just getting started, uh, maybe you found a new pitfall that others should avoid. Uh, so you can bring along those cautionary tales and share those with, with the global community. I think it's so important that we continue to learn from and, and be inspired by one another. 

Kovid Batra: Totally. I totally agree with that. All right. So I think, we'll just get started with you, Nathen. Uh, so I think the first thing that I want to talk about is very fundamental to the implementation of DORA, right? We know lately we had a Gartner report saying there were only 5 to 10 percent of teams who actually implement such frameworks through tools or through processes in their, in their organizations. Whereas, I mean, I have grown up in my professional career hearing that if we are measuring something, only then we can improve it. So if you go to any department or any, uh, business unit for that matter, everyone follows some sophisticated processes or tooling to measure those KPIs, right? Uh, why is it, why this number is so low in our engineering teams? And if let's say, they are following something only through What's the current landscape according to you? I mean, you have been such a great believer of all this data-driven DORA metrics, engineering metrics, SPACE. So what's, what's your thought around it? 

Nathen Harvey: Yeah, it's a, it's a good question. And I think it's really interesting to think about. I think when you look at the practice of software engineering or development, or even operations like reliability engineering and things along those lines, these all tend to be, um, one creative work, right? When you're writing software, you're probably writing things that have never been written before. You're trying to solve a new problem that's very specific to your context. Um, it can be very difficult to measure, what does that look like? I mean, we've, we've used hundreds of different measures over the years. Some are terrible. You know, I think back to a while ago, and hopefully no one watching is under this measurement today. But how many lines of code did you commit to the repository? That's, that's a measure that has certainly been used in the past to figure out, is this a develop, is this developer being productive or not? Uh, we all know, hopefully by now that that's a, it's a terrible way to measure whether or not you're delivering value, whether or not you're actually being productive. So, I think that that's, that's part of it. 

I also think, frankly, that, uh, until a few years ago, the world was working in a, in a way in which finances were easy to get. We were kind of living in this zero interest rate, uh, world. Um, and engineers, you know, we're, we're special. We do work that is, that can't be understood by anyone else because we have this depth of knowledge in exactly what we're doing. That's kind of a lie. Uh, those salespeople, those marketing people, they have a depth of knowledge that we don't understand, that we couldn't do their job in the same way that they couldn't do our job. And that's, that's not to say that one is better than the other, or one is more special than the other, but we absolutely need different ways to measure. And even ways that we have to measure other sort of disciplines, uh, don't actually give us the whole picture. Take sales, for example, right? You might look at well, uh, how much, uh, how much revenue is this particular salesperson bringing in to the organization? That is certainly one measure of the productivity of that salesperson, but it doesn't really give you the whole picture, right? How is that salesperson's experience? How are the people that are interacting with that salesperson? How is their experience? So I think that it is really difficult to agree on a good set of measures to understand what those measures are. And frankly, and this, this might be a little bit shocking, Kovid, but look, I, I, I am a big proponent of DORA and the research and everything that we've done here. But between you and me, I don't want you to do DORA metrics. I don't want you to. I don't care about the DORA metrics. What I care about is that you and your team are improving, improving the practices and the processes that you have to deliver and operate software, improving the well-being of the members of your team, improving the value that you're creating for your business, and improving the experience that you're creating for your customers.

Now, none of those are the DORA metrics. Of course, Measuring the DORA metrics helps us assess some of those things and what we've been able to show through the research is that improving things like software delivery performance have positive outcomes or positive predictive nature of better organizational success, better customer satisfaction, better well-being for your teams. And so, I think there's there's this point where, you know, there's, uh, maybe this challenge, right, do you want, do you want me to spend as an engineer? Do you want me to spend time measuring the work that I'm doing, measuring how much value am I delivering, or do you want me delivering more value? Right? And it's not really an either or trade-off, but this is kind of some of the mindsets I have. And I think that this is some of the, the blockers that come in place when people want to try to bring in a measurement framework or a metrics framework. And then finally, Uh, you know, between you and me, nobody really likes their work to be measured. I want to feel like I'm providing valuable work and, and know that that's the case, but if you ask me to measure it, I start to get really worried about why are you asking that question. Are you asking that question because you want to give me a raise and a promotion and more money? Great. I'm gonna make sure that these numbers look really good. If you're asking that question to figure out if you need to keep me on board, or maybe you can let me go, now I'm getting really nervous about the questions that you're asking.

And so I think there's a lot of like human nature in the prevention of adopting these sorts of frameworks. And it really gets back to, like, who are these frameworks for? And again, I'll just go back to what I said sort of towards the beginning. I don't want you to do DORA metrics. I want you to improve. I want you to get better. And so, if we think about it in that perspective, really the DORA metrics are for me and my teammates. They aren't necessarily for my leaders. Because it's me and my teammates that are going to make those improvement efforts. 

Kovid Batra: Totally. I think, um, very wise words there. One thing that I just picked up from what you just said, uh, from the narrative, like there is a huge organizational cultural play in this, right? People are at the center of how things get implemented. So, you have been experiencing this with a lot of teams. You have implemented this. What's the difference that you have seen? What are those mindsets which make these things implement actually? What are those organizational factors that make these things implement? 

Nathen Harvey: Yeah, that's a, that's a good question. I would say, first it starts with, uh, the team that you're going to start measuring, or the application, the group of people and the technology that you want to start measuring. First, these people have to want to change, because if we're, if we're going to make a measure on something, presumably we're making that measure so that we understand how we are, so that we can improve. And to improve, we have to change something. So it starts with the people wanting to change. Oh, except I have to be honest, that's not enough. Wanting to change actually isn't enough. We all want to change. We all want to get better. Actually, maybe we all just want to get better, but we don't want to have to change anything. Like I'm very comfortable in the way that I work. So can it, can it just produce better results? The truth is, I think we have to find teams that need to change. There has to be some. Motivating factor that's really pushing them to change because after we look at the dashboard, after we see some numbers, if we're not truly motivated, if there isn't a need for us to change, we're probably not going to change our behavior. So I think that's the first critical component is this need to improve, this fundamental desire that goes beyond just the desire. It's, it's a motivating factor. You have to do this. You have to get better because the competition is coming after you, because you're feeling burnt out, because for a myriad of reasons. So I think that that's a big first step in it. 

Kovid Batra: A lot of times, what I have seen while talking to a lot of my Typo clients also, uh, is, uh, they feel that there is a stage when this needs to be implemented, right? So people use Git metrics, Jira metrics to make sure things are running fine. And I kind of agree to them, like very small teams can, can rely on that. Like maybe under 10 size teams are good. But, what do you think, uh, what, what's the DevOps maturity? What's the team size that impacts this, where you need to get into a sophisticated framework or a process like DORA to make sure things are, uh, in, in the right visibility? 

Nathen Harvey: Yeah, that's, that's, that's a really good question. And I think unfortunately, of course, the answer is it, it depends, right? It is pretty context-specific. I do think it matters that, uh, it matters the level at which you're measuring these things. You know, the DORA metrics have always been meant, and if you look at our survey, we always sort of prepend our questions with, for the primary application or service that you're working on. So when we think about those DORA metrics, those software delivery metrics in particular, we aren't talking about an organization. What is the, you know, we don't ask, for example, what is the deployment frequency at Typo? But instead, we ask about specific applications within Typo, and we expect that you're going to have variation across the applications within your team. And so, when you have to get into this sort of more formal measurement program, I think that really is context-specific. It really depends on the business and even what are you measuring? In fact, if if your team has, uh, more of a challenge with developing code than they do with shipping code, then maybe the DORA metrics aren't the right metrics to start with. You want to sort of find your constraint within your organization, and DORA is very much focused on software delivery and operational performance. So on the software delivery piece, it's really about are we able to take this code that was written and get it out the door, put it in front of customers. Of course, there's a lot of things on the development side that enable that. There's a lot of things on the operational side that benefit from that. It all kind of comes together, but it is really looking at finding that particular pain point or friction point within your organization. 

And then, I think one other thing that I'll just comment on really quickly here is that as teams start to adopt these frameworks, there's often an overfitting for precision. We need precise data when it comes to this. And honestly, again, if you go back to the methods that DORA uses, each year we run an annual survey. We ask people, what is your average time or your typical time from code committed to code in production? We're not hooking into your Git systems or your software delivery pipelines or your, uh, task backlog management systems. We're not hooking into any of those things. We're asking about your experience. Now, we have to do that given that we're asking the entire world. We can't simply integrate with all of those systems. But this level of precision is very helpful at some point. But it doesn't necessarily need to be where you start. Right? Um, I always find it's best to start with a conversation. Kind of like what we're having today. 

Kovid Batra: But yeah, I think, uh, the toolings that are coming into the, into the picture now are solving that piece also. So I think both the things are getting, uh, balanced there because I feel the survey part is also very critical to really understand what's going on. And on top of that, you have some data coming from the systems without any effort that reduces your pain and trust on what you are looking at. So yeah, that makes sense. 

Nathen Harvey: Yeah, absolutely. And, and, and there is a cautionary tale built in there. I've seen, I've seen too many teams go off and try to integrate all of these systems together to get all of the precise data and beautiful dashboards. Sometimes that effort ends up taking months. Sometimes that effort ends up taking years. But what those teams fail to do over those months or years is actually try to improve anything. All they're trying to improve is the precision of the data that they have. And so, at the end of that process, they have more precise, a more precise understanding of what they knew at the beginning of that process.

And they haven't made any improvements. So that's where a tool like Typo, uh, or others of this nature like really come in because now I don't have to think about as much, all of that integration, I can, I can take something off the shelf, uh, and run it in my systems and immediately start to get value from that. 

Kovid Batra: Totally. I think, uh, when it comes to using the product, uh, Ido has been, uh, one of the people who has connected with me almost thrice in the last few days, giving me some feedback around how to do things. And I would let Ido have some of his, uh, questions here. And, uh, I have, uh, my demo dashboard also ready. So if there is anything that you want to refer back to Ido or Nathen, to like highlight some metrics that they can look at, I, I'll be happy to share my screen also. Uh, over to you, Ido. I'll, I'll put you on the main screen so that the audience sees you well. 

Ido Shveki: Oh, thanks, Kovid. And hi again, Nathen. Uh, first of all, very interesting, uh, and you speaking about it. I also find this topic, uh, close to my heart. So I, uh, I, it's a fascinating to hear you talk about it. I wanted to know, uh, if you have any, you mentioned before that among the different, like you said, it may be inside the Typo as a company, there are like different benchmarks, different, uh, so how can you identify this, uh, benchmark? Maybe my questions are a bit practical, but let me know if that's the case, but yeah, I just want to know how to identify this benchmark because as you mentioned, and also at BeeHero, we have like, uh, uh, different teams, different sizes, different maturity, uh, different, uh, I mean, uh, seniority level. So how can I start with these benchmarks?

Nathen Harvey: Yeah, yeah. That's a, that's a really great question. So, um, one of the things that I like to do when I get together with a new team is we first kind of, or a new organization first, let's, let's pick an application or two. So at, BeeHero, uh, I, I, I know very little about what BeeHero does, you know, tell us a little bit about BeeHero. Give us, give us like a 30-second pitch on BeeHero. What do you do there? 

Ido Shveki: Cool. So we are an Israeli startup where we deal with agriculture. What we do is we place, uh, sensors inside beehives as the, as the name might, uh, you know, give you a hint. Uh, we put sensors inside beehives and this way we can give a lot of, uh, we, we collect metrics and we give great, uh, like, uh, good insights, interesting insights to beekeepers, uh, so that they can know what to do with their bee colony, how to treat it, and how to maintain the bee colony. So, this is, you know, basically, and if, if I'm, uh, to your question, so we have, yeah, uh, different platforms. We have the infra platforms, we have the firmware guys, we have mobile app, et cetera. So. But I assume that like every company has this, different angles of a product. 

Nathen Harvey: Yeah. Yeah. Yeah. Of course. Every company has hundreds, maybe thousands of different products that they're maintaining. Yeah, for sure. Um, not, well that's first, that's super cool. Um, keeping the farmers and the bees happy. Now, so what I like to do with, with a new team or organization that I'm working with is we start with an application on our service. So maybe, maybe we take the mobile application that BeeHero has and what we want to do is bring together, in the perfect world, we bring together into a physical room, everyone that's responsible for prioritizing work for that application, designing that work, writing the software, shipping the software, running the service, answering customer requests, all of that stuff. Uh, perhaps we'd let the bees stay in the hives. We don't bring them into the room with us. Um, software engineers aren't, aren't known for being good with bees, I guess. So, but.. 

Ido Shveki: They do affect the metrics though. Yeah, I don't want, I don't want that. 

Nathen Harvey: Absolutely. Absolutely. So, so we'll bring these people together. And I like to just start with a conversation, uh, at dora.dev, we have a quick check, that allows you to quickly answer those for software development or software delivery performance metrics. You know, the deployment frequency, change lead time, your change failure rate and your failed deployment recovery time. But even before we get to those metrics, I like to start with a simpler question. Okay, so together as a team, a developer has just committed a change to the version control system. As a team, let's go to the board and let's map out every step in the process, every handoff that has to happen between that code commit and that code landing in production, right, so that the users can use it. And the reason we bring together a cross-functional team is because in many organizations, I don't know how big BeeHero is, but in many organizations, there are handoffs that happen from one team to the next, sort of that chain of custody, if you will, to get to production. Unfortunately, every single one of those handoffs is an opportunity for introducing friction, for hiding information, you know. I've, I've worked with teams as an example where the development team is responsible for building a package, testing that package and then they hand it off to the test team. Well, the test team does, takes that package and they discard it. They go back to the Git repo. They actually clone the Git repo and then they build another package and then start testing that. So now, the developers have built a package that gets discarded. Now the testers build another package that they test against that probably gets discarded and then someone else builds a third package for production. So there's, as you can imagine, there's lots of ways for that handoff and those three different packages to be different from one another. This is, it's, it's mind boggling. But until we put all those people in the room together, you might not even see that friction and that waste in the process. So I start there to really identify where are those friction points? Where are those pain points? And oftentimes you have immediate sort of low hanging fruit, if you will, immediate improvement opportunities.

And the most exhilarating part of that process as a facilitator is to see those aha moments. "Oh my gosh! I didn't realize that you did that." "Oh, I thought I packaged it this way so that you could do this thing that you're not even doing. You're just rubber stamping and passing it on." Or whatever it is. Right? So you find those things, but once you've done that map, then you go back to those four questions. How's my, what are my, you know, we used a quick check in that process. What does my software delivery performance look like? This gives us a baseline. This is how we're doing today. But in this process, we've already started to identify some of those areas for improvement that we want to set next. Now I do this from one team to the next or encourage the teams to do this on their own. And this way we aren't really comparing, you know, what is your mobile app look like versus the front end website, right? Should they have the same deployment frequency? I don't know. They have different customers. They have different needs. They have different teams that are working on them. So you expect them to be different. And the thing that I don't really care about over time is that everyone gets to the top level or a consistent performance across all of the teams. What I'd much rather see is that everyone is improving over time, right? So in other words, I'd rather reward the most improved team than the team that has the highest performance. Does that make sense? 

Ido Shveki: Yeah, a lot actually. 

Nathen Harvey: All right. 

Ido Shveki: Thanks. 

Nathen Harvey: Awesome. Yeah. 

Ido Shveki: Kovid, do we have another, time for another question? 

Kovid Batra: Yeah, I do. I mean, uh, you can go ahead, please. Uh, we have another three minutes. Yeah. 

Ido Shveki: Oh, cool. I'll make it quick. I'm actually interested in how do you aligned the business to DORA metrics? Because I usually I find myself talking to the management, CEO, CTO, trying to explain to them what's, what's happening under the hood in the developer team and it's not always that easy. Do you have some tips there?

Nathen Harvey: Yeah, you know, as your CEO come to you and said, You know, you know, last year you did 250 deploys. If you do 500 this year, I'm going to double your salary. They probably never said that to you. Did that? 

Ido Shveki: No, no. 

Nathen Harvey: No, no. Primarily because your CEO probably doesn't care how many deploys you delivered. Your CEO. 

Ido Shveki: And I think that's, I mean, I wouldn't want them to. 

Nathen Harvey: You don't want them to. You're, you're exactly right. But they do care about other things, right? They care about, I don't, I don't know, I'm going to make up some metrics. They care about how many, uh, like the health of the hives that each farmer has, right? Like, that's what they care about. They care about how many new farmers have signed up or how many new beekeepers have signed up, what is their experience like with BeeHero. And, and so really, as you go to get your executives and your management and, and the business tied into these metrics, it's probably best not to talk about these metrics, but better to talk in terms of the value that they care about, the measures that they care about. So, you know, our onboarding experience has left some room for improvement. If we ship software faster, we can improve that onboarding experience. And really it's a hypothesis. We believe that by improving our software delivery performance, we'll be able to respond faster to the market needs, and we'll be able to therefore improve our onboarding process as an example, right? And so now you can talk to your CEO or other business counterparts about look, as we've improved these engineering capacities and capabilities, we've seen this direct impact on our customers, on the business value that we care about. DORA shows, through our data collection, that software delivery performance is predictive of better organizational performance. 

But it's up to you to prove that, right? It's up to you, essentially, we encourage you to replicate our study. We see this when we look across teams. Do you see this on your team? Do you see that improving? And that's really, I think, how you should talk about it with your business counterparts. And frankly, um, you, you are the business as well. So it also encourages you and the rest of the engineers on your team to remember, we aren't creating this application because we want to use the new, uh, serverless technology, or we want to play with the latest, greatest AI. We're building this application to help with the health of bees, right? And so, keeping that connection back to the business, I think is really important. 

Kovid Batra: Okay. On your behalf, can I ask one question? 

Yeah. So I think, uh, there are certain things that we also struggle with, with not just Ido, but, uh, various other clients also that, which metrics to pick up. So can we just run through a quick example from your, uh, history of clients where you have, uh, probably highlighted for, uh, let's say, a 100-member dev team. What metrics make sense in what scenario? I, I'll quickly share my screen. Uh, I have some metrics highlighted for, for DORA and more than that on Typo. 

Nathen Harvey: Oh, great! 

Kovid Batra: You can tell me which metrics one should look at and how one should navigate through it. 

Nathen Harvey: Yeah, for sure. That, that'd be awesome. That'd be awesome. So I think as you're pulling up your screen, I'll just start with, you know, the, the reason that the DORA software delivery metrics are nice, is kind of multifold, right? First, there's only four of them. So you can, you can count them on one hand. That's, that's a good thing. Uh, you aren't over, like over, have too many metrics that you just don't know. How many, which lever should we pull? There's too many in front of me, right? Second. Um, they, they represent both lagging and leading indicators. In other words, they're lagging indicators for what does your engineering process look like? What's engineering excellence or delivery excellence look like within your organization? These DORA metrics can tell you. Those are the lagging indicators. You have to change things over here to make them improve. But they're leading indicators for those business KPIs, right? Organizational performance, well-being for the people on your team. So as we improve these, we expect those things to improve as well. And so, the nice thing about starting with those four metrics is that it gives you a good sense of where you are. Gives you a nice baseline. 

And so, I'm just going to make my screen a little bit bigger so I can see your, uh, yeah, that's much better. I can see your dashboard now. All right. So you've got, uh, you've got those, uh, looks like those four, uh, a couple of those delivery metrics you got, uh, oh, actually tell me what, what do you have here, Kovid? 

Kovid Batra: Yeah. So we have these four DORA metrics for us, the cycle time, deployment frequency, change failure rate, and mean time to restore. So we also believe in the same thing where we start off with these fundamental metrics. And then, um, we, we have more to deep dive into, like, uh, you can see things at team level, so there are different teams in one single view where you can see each team on high level, how their velocity, quality, and throughput looks like. And when you deep dive, you find out those specific metrics that basically contribute to velocity, quality, and throughput of the teams. And these are driven from DORA and various other metrics that we realized were important and critical for people to actually measure what's going on.

Nathen Harvey: Yeah. Yep, that's great. And so I really like that you can see the trend over time because honestly, the, the single number doesn't really mean anything to you. It's like getting on the scale in the morning. There's a number on the scale. I don't know if that's good or bad. It depends on what it was yesterday and what it will be tomorrow. So seeing that trend is the really important thing here because then you can start to make decisions and commitments as a team on experiments that you want to run, right? And so in this particular case, you see your cycle time going up. So now what I want to do is kind of dig in. Well, what's, what's behind the cycle time, what's causing this? And that's where the things like the, that map and, and you see here, we've got a little map that shows you exactly sort of what happens along that flow. So let's take a look at those. We have coding, pick up, review and merge, right? Okay, yup. And so the, nice thing there is that the pickup seems like it's going pretty well, right? One of the things that we found last year in our survey was that teams with faster code reviews have 50 percent better software delivery performance. And so it looks like this team is doing pretty good job. I imagine that pickup is you're reviewing that code, right? 

Kovid Batra: Yeah. Yeah. Yeah. 

Nathen Harvey: Mm hmm. Yeah. So, so that's good. It's good to see that. But what's the review? Oh, I see. So pickup must be when you first grab the PR and then review maybe incorporates all the sort of back and forth feedback time. 

Kovid Batra: Yes, yes. And finally, when you're merging it to your main branch, so the time frame between that is your review time. 

Nathen Harvey: Ah, gotcha, gotcha, gotcha. Okay, so for me, this would be a good place to dig in. What's, what's happening there? Because if you look between that pickup and review, that's about 8 hours of your 5, 10, 15, uh, 18 hours. So it's a significant portion there is, sort of in that code review cycle. This is something I'd want to look at. 

Kovid Batra: Perfect. 

Nathen Harvey: Yeah. Yeah. And we see this, we see this a lot. Um, one, one organization I worked with, um, the, the challenge that they had was not necessarily in code review, but in approvals, they were in a regulated industry and they sent all changes off to a change approval board that had to approve them, that change approval board only met so frequently, as you can imagine, that really slowed down their cycle time. Uh, it also did not help with their stability, right? Um, changes were just as likely to fail when they went to production as not, uh, regardless of whether or not they went through that change approval board. So we really looked at that change approval process and worked to help them automate that. The net result is I think they're deploying about 600 times more frequently today than they were before we started the process, which is pretty incredible. 

Kovid Batra: Cool. That's really helpful, Nathen. And thanks for those examples that fit into the context of a lot of our audience here. In fact, this question, I just realized was asked by Benny Doan also. So I think he would be happy to hear you. And, uh, I think now it's time. I feel the audience can ask their questions. So, um, we'll start with a 15 minute Q&A round where all the audience, you are free to comment in the comment sections with all the questions that you have. And, uh, Nathen, Ido, uh, would be happy to listen out to you on those particular questions. 

Ido Shveki: Kovid, should we just start answering these questions? 

Kovid Batra: Yeah. 

Nathen Harvey: Yeah, I'm having trouble switching to the comments tab. So maybe you could read some of the questions. I can't see them. 

Ido Shveki: Um, I can see a question that was also asked by Benny, which I worked in the past. Oh, hi, Benny. Nice that you're here. Um, about how to, like, uh it was by Nitish and Benny as well, asking about how does the, the dev people, the developers won't feel micromanaged when we are using, um, uh, the DORA metrics with them. Um, I can begin, I'll let you Nathen, uh, uh, elaborate on it in a second. I can begin with my experience thing that first of all, it is a slippery slope. I mean, I do find it not trivial to, to, um, like if you would just show them that I'm looking at the times from this PR to the improvement and line of codes, et cetera, like Nathan said in the beginning. Yeah. I mean, they would feel micromanage. Um, I, uh, first of all, I, I usually talk about it on a, on a team level or an organization level. And when I do want to raise this, uh, questions or maybe like address them as a growth opportunities for a certain developer. Uh, personally, I don't look at it as a, like criticism. I, it's like a, it's a beginning of a conversation. It's not like I don't know. I didn't make up my mind before. Uh, and because of this metric looks like this, then I'm not pleased with how you perform. It's just like, All right. I've seen that there is a decrease here. Uh, is there a reason? Let's talk about, let's discuss it. I'm easily convinced if there are like, uh, ways to be convinced. And, but, but yeah, I do look at it as a growth. Um, I try to, to, to convince and I do look at it as a, like a, growth opportunity for the developer to, to look at, uh, yeah, that's, that's at least my take over this. 

Nathen Harvey: Yeah, I definitely agree with that, you know, because I think that this question really gets to a question of trust. Um, and how do you build trust with your teammates? And I think the way that you build trust is through your actions. Right? And so if you start measuring and then start like taking punitive action against individual developers or even teams, that's going to, your actions are going to tell people, you should be afraid of these metrics. You should do whatever you can to not be measured by these metrics, right? But instead, if and DORA talks a lot about culture, if you lean in and use this as an opportunity to improve, an opportunity to learn more about how the team is going. And, and I like your approach there where you're taking sort of an inquisitive approach. Hey, as an example, you know, Hey, I see that the PRs, uh, that you started to submit fewer PRs than you have in the past, what's going on? It may be that that person has, for the time being, prioritized code reviews. So they're doing less PRs. It may be that they're working on some new architectural thing. They're doing less PRs. It may be that, uh, they've had a family emergency and they've been out of the office more. That's going to lower their PRs. That's the, the, the fact that they have fewer PRs is not enough information for you to go on. It is a good place to start a conversation. 

And then, I think the other thing that really helps is that you use these metrics at the team level. So if you as a team start reviewing them, maybe during your regular retrospectives or planning sessions, and then, importantly, it comes back to what are you going to change? Is the team going to try something different based on what they've learned from these metrics? Oh, we see that our lead time is going up, maybe our continuous integration practices, we need to put some more effort into those or some more automated testing. So over the next sprint or time block, we're going to add, you know, 20 percent more capacity for automated testing. And let's see how that impacts things. So seeing that these metrics are being used to inform improvements, that's how you prevent that slippery slope, I think. 

Kovid Batra: Totally. Okay. I think we can move on to this next question. Uh, this is Nitish. Uh, how can DORA and data-driven approach be implemented in a way that devs don't feel micromanaged? Yeah, I think. 

Nathen Harvey: Yeah, I think, I think we've covered a little bit of this in the previous question here, Nitish. And I think that it really comes back to remembering that these are not measures that should be at the individual level. We're not asking, Kovid, what's your deployment frequency? You know, what's yours? Oh, one of you is better than the other. Something's going to change. No, no, no. That's not how we, that's not how we use these measures. They're really meant for that application or service level. When it comes to developing, delivering, operating software or any technology, that's a team sport. It's not an individual sport. 

Kovid Batra: All right. Then we have from Abderrahmane, uh, how are the market segments details used for benchmark collected? 

Nathen Harvey: Yeah, this is a really good question. Thanks for that. So, uh, as you know, we run a survey each year and we ask about what industry are you in? Um, and what we found surprisingly, maybe, maybe not surprisingly is that over the years, industry is not really a determinant of how your software delivery performance is going to be. In other words, what we see across every industry, whether it's technology or retail or government or finance, we see teams that have really good software delivery performance. We also see in all of those industries, teams that have route rather poor software, but lots of opportunities to improve their software delivery performance, I should say. Yeah. 

So we see that, uh, the market segments are there and, and honestly, we, we publish that data so that people can see that, look, this can happen in our industry too. Um, I always worry that, you know, someone might use their industry as a reason not to question the status quo. Oh, we're in a regulated industry, so we can't do any better. It doesn't matter what industry you're in. You can always do better. You can always do worse as well. So just be careful, like focus on that improvement. 

Kovid Batra: Cool. Uh, next question is from Thomas. Uh, how do you plan the ritual with engineers and stakeholders when you're looking at this metric? Yeah, this is a very, uh, important question. I think Nathen, would you like to take this up? 

Nathen Harvey: Yeah, I'll take this. I'd love to hear how Ido is doing this as well, sort of incorporating the metrics into their daily work. But I think it's, it's, it's just that as you go into your planning or retrospective cycle, maybe as a team, you think about the last period and you pull up maybe the DORA quick check, or if you're using Typo or something like it, you pull up the dashboard and say, "Look, over the last two weeks over the last month, here's where we're trending. What are we going to do about that? Is there something that we'd like to change about that? What can we learn about that?" Start just with those questions. Start thinking about that. So I think really just using it as a, as a discussion point in those retrospectives, maybe an agenda item in those retrospectives is a really powerful thing that you can do.

Ido, what's your experience? 

Ido Shveki: Yeah. So, um, I totally agree. And I think in most parts, this is what I, what I also do, we're also doing at BeeHero, in their perspectives, maybe not on, bi-weekly basis, like every two weeks because sometimes they find it too often and not too much to, to, you know, to, uh, I want it to be, uh, tell them something new, let's say. Um, but also I do find it in what we, when we are doing some rituals for some incident that happened and we're discussing this issue, I really put emphasis, and I think this is the cultural part that you mentioned before. Uh, in this rituals of, uh, incident, uh, rituals. I really try to point out and look at, uh, uh, how long did it take us to mitigate it? Um, when, like how, how long, uh, until the, the customer didn't see the issue. And from these, uh, points, you can, I mean, I hope the team understands the culture that I'm pushing towards. And from that point, they will also want to implement DORA metrics without even knowing the name DORA. We don't really care about the name. I mean, we don't really, it doesn't really matter if they know how to call it. Just like to, as you mentioned before, I don't want you to know about DORA. Just get better or just be better at this. So yeah, that's basically it. 

Nathen Harvey: Thanks. Awesome. 

Kovid Batra: All right. I think there is one thing that I wanted to ask from this. It's good with the engineers, probably, and you can just pull in every time. But when it comes to other stakeholders in the business, what I have seen and experienced with my clients is they find it hard to explain these DORA metrics in terms of the business language. I think Nathen, you touched upon this in the beginning. I would like to just highlight this again for the audiences' sake. 

Nathen Harvey: Yeah, I think that, I think that's really important. And I think that when it comes to dashboards, uh, it, it would be really good to put your delivery performance metrics right next to your organizational performance metrics, right? Are we seeing better customer, like, are we seeing the same trend? As software delivery improves, so do customer signups, so do, uh, revenue that we get per customer or something along those lines. That's, you know, if you think about it, we're really just trying to validate an experiment. We think that by shipping this feature, we're going to improve revenue. Let's test that. Let's look at that side-by-side. 

Kovid Batra: Totally. All right. Uh, we have a lot of questions coming in. Uh, so sorry, audience, I'm not able to pick all of those because we are running short on time. We'll pick one last question. Uh, okay. That's from Julia. Uh, are there any variations of DORA metrics you have found in customer deployed or installed software? Example, deployment frequency may not be directly relevant. A very relevant question. So yeah. 

Nathen Harvey: Yeah, absolutely. Uh, I think, I think the beauty of the four key metrics is that they are very simple, except they're not, they are very simple on the surface, right? If, and if you take just, let's just take one of them, change lead time. In DORA's language, that starts at like change is committed and it ends when that changes in production. Okay. What does committed mean? Is it committed to a branch? Is it committed to the main line? Is that branch, has that branch been merged into the main line? Who knows? Um, I have a perspective, but it doesn't really matter what my perspective is. When it comes to production. What does it mean to be in production? If we're doing, um, progressive deploys, does it mean the first user in production has it or only when 100 percent of users have it? Is that when it's in production? Or somewhere in between? Or we're running mobile applications where we ship it off to the app store and we have to wait for it to get approved, or installed software where we package it up and we shrink wrap it into a box and we ship out a CD. Is that deployed? I mean, I don't, I don't know that anyone does that any, well, I'm sure it happens. I know in the Navy they put software on helicopters and they fly it out to ships. So that's, you know, all of these things happen. Here's the thing. For your application, what you need to do is think about those four metrics and write down for, for this application, commit, change, change lead time starts here at this event ends here at that event. We're going to write that down probably in maybe something like an architectural decision record and ADR, put it into the code base. And as you write it down, make sure that it's clear, make sure that everyone agrees to it, and probably just as importantly, make sure that when you write it down, you also write down the date at which we will revisit this decision, right? Because it doesn't have to be set in stone. Maybe this is how we're going to measure things starting today, and we'll come back to this in six months. And some of the things that drive that might be the mechanics of how you deliver software. Some of the things that drive that might be the data that you have access to, right? And over time, you may have access to more precise data, additional data that you can then start to use in that. So the important thing is that you take these metrics and you contextualize them for your team. You write down what those metrics are, what their definitions are for your team and you revisit those decisions over time. 

Kovid Batra: Perfect. Perfect. All right. I think, uh, it's already time. Nathen, would you like to take one more question? 

Nathen Harvey: Uh, I'm happy to take one more question. Yes. 

Kovid Batra: All right. All right. So this is going to be the last one. Sorry if someone's question is not being asked here. But let's, let's take this up. Uh, this is Jimmy. Uh, do you ever try to map a change in behavior/automation/process to a change in, in the macro-DORA performance? Or should we have faith that our good practices is what is driving positive DORA trends? 

Nathen Harvey: Um, I think that, uh, having faith is a good thing to do. Uh, but validating your experiments is an even better thing to do. So, uh, as an example, uh, let's see, trying to change a behavior automation process to a change in the macro performance. Okay. Uh, I'll, I'll, I'll pick a change that you might have or an automation that you might have. Let's say that, uh, today, your deployment process is a manual process, uh, and you're doing, and there's lots of steps that are manual, uh, and you want to automate that process. Uh, so, we can figure out what are our, what's our software delivery performance look like today, you can use a Typo dashboard, you could use the DORA quick check. Write that number down. Now make some investments in automation, deployment automation, you've deployed, instead of having 50 manual steps, you now have 10 manual steps that you take and 40 that have been automated. Now let's go back and remeasure those DORA performance metrics. Did they improve? One would think and one would have faith that they will have improved. You may find for some reason that they didn't. But, validating an experiment and invalidating an experiment are kind of the same thing. In either case, it's really about the approach that you take next. Are you using this as an opportunity to learn and decide how are we going to respond to the, the new information that we have? It really is about a process of continuous learning, And hopefully continuous improvement, but with every improvement, there may be setbacks along the way. 

Kovid Batra: Great. All right. On that note, I think that's our time. We tried to answer all the questions, but of course we couldn't. So we'll have more sessions like this, uh, to help all the audience over here. So thanks a lot. Uh, thank you for being such a great audience. Uh, we hope this session helped you build some great confidence around how to implement DORA metrics in your teams.

And in the end, a heartfelt thanks to my cohosts, Nathen and Ido, and to my Typo team who made this event possible. Thanks a lot, guys. Thank you. 

Nathen Harvey: Thank you so much. Bye bye. 

Ido Shveki: Thanks for having us. Bye. 

Top Software Development Metrics (2024)

What are Software Development Metrics?

Software metrics track how well software projects and teams are performing. These metrics help to evaluate the performance, quality, and efficiency of the software development process and software development teams' productivity. Hence, guiding teams to make data-driven decisions and process improvements.

Importance of Software Development Metrics:

  • Software engineering metrics evaluate the productivity and efficiency of development teams, ensuring that projects are progressing as planned.
  • These metrics ensure that the projects are progressing as planned and potential bottlenecks are taken into consideration as early as possible.
  • Software quality metrics help to identify areas for improving software quality and stability.
  • These metrics monitor progress, manage timelines, and enable software developers to make informed decisions about project scope and deadlines.
  • Regular reviewing and analysis of metrics allow team members to identify weaknesses and optimize processes for better performance and efficiency.
  • Metrics assist in understanding resource utilization which leads to better allocation and management of development resources.
  • Software engineering metrics related to user feedback and satisfaction ensure that the software meets user needs and expectations and drives enhancements based on actual user experience.

Process Metrics

Process Metrics are quantitative measurements that evaluate the efficiency and effectiveness of processes within an organization. They assess how well processes are performing and identify areas for improvement. A few key metrics are:

Development Velocity

Development Velocity is the amount of work completed by a software development team during a specific iteration or sprint. It is typically measured in terms of story points, user stories, or other units of work. It helps in sprint planning and allows teams to track their performance over time.

Lead Time for Changes

Lead Time for Changes is a measure of time taken by code changes to move from inception to deployment. It tracks the speed and efficiency of software delivery and provides valuable insights into the effectiveness of development processes, deployment pipelines, and release strategies.

Cycle Time

This metric measures the total elapsed time taken to complete a specific task or work item from the beginning to the end of the process. It Helps assess how quickly the team can turn around tasks and features, identify trends and failures, and forecast how long future tasks will take. 

Change Failure Rate

Change Failure Rate measures the percentage of newly deployed changes that caused failure or glitches in production. It reflects reliability and efficiency and relates to team capacity, code complexity, and process efficiency, hence, impacting speed and quality. 

Performance Metrics

The software performance Metrics quantitatively measure how well an individual, team, or organization performs in various aspects of their operations. They offer insights into how well goals and objectives are being met and highlight potential bottlenecks. 

Deployment Frequency

Deployment Frequency tracks how often the code is deployed to production. It measures the rate of change in software development and highlights potential issues. A key indicator of agility and efficiency, regular deployments indicate a streamlined pipeline, which further allows teams to deliver features and updates faster.

Mean Time to Restore

Mean Time to Restore measures the average time taken by a system or application to recover from any failure or incident. It highlights the efficiency and effectiveness of an organization’s incident response and resolution procedures.

Code Quality Metrics

Code Quality Metrics measure various aspects of the code quality within a software development project such as readability, maintainability, performance, and adherence to best practices. Some of the common metrics are: 

Code Coverage

Code coverage measures the percentage of a codebase that is tested by automated tests. It helps ensure that the tests cover a significant portion of the code, and identifies untested parts and potential bugs.

Code Churn

Code churn measures the frequency of changes made to a specific piece of code, such as a file, class, or function during development. High code churn suggests frequent modifications and potential instability, while low code churn usually reflects a more stable codebase but could also signal slower development progress.

Focus Metrics

Focus Metrics are KPIs that organizations prioritize to target specific areas of their operations or processes for improvement. They address particular challenges or goals within the software development projects or organization and offer detailed insights into targeted areas. Few metrics include: 

Developer Workload 

Developer Workload represents the count of Issue tickets or Story points completed by each developer against the total Issue tickets/Story points assigned to them in the current sprint. It helps to understand how much work developers are handling, and crucial for balancing workloads, improving productivity, and preventing burnout. 

Work in Progress (WIP) 

Work progress represents the percentage breakdown of Issue tickets or Story points in the selected sprint according to their current workflow status. It highlights how much work the team handles at a given time, which further helps to maintain a smooth and productive workflow. 

Customer Satisfaction 

Customer Satisfaction tracks how happy or content customers are with a product, service, or experience. It usually involves users' feedback through various methods and analyzing that data to understand their satisfaction level. 

Technical Debt

Technical Debt metrics measure and manage the cost and impact of technical debt in the software development lifecycle. It helps to ensure that most critical issues are addressed first, provides insights into the cost associated with maintaining and fixing technical debt, and identifies areas of the codebase that require improvement.

Test Metrics

Test Coverage

Test coverage measures percentage of the codebase or features covered by tests. It ensure that tests are comprehensive and can identify potential issues within the codebase which further improves quality and fewer bugs.

Defect Density

This metric measures the number of defects found per unit of code or functionality (e.g., defects per thousand lines of code). It helps to assess the code quality and the effectiveness of the testing process.

Test Automation Rate

This metric tracks the proportion of test cases that are automated compared to those that are manual. It offers insight into the extent to which automation is integrated into the testing process and assess the efficiency and effectiveness of testing practices.

Productivity Metrics

This software metric helps to measure how efficiently dev teams or individuals are working. Productivity metrics provide insights into various aspects of productivity. Some of the metrics are:

Code Review Time

This metric measures how long it takes for code reviews to be completed from the moment a PR or code change is submitted until it is approved and merged. Regular and timely reviews foster better collaboration between team members, contribute to higher code quality by catching issues early, and ensure adherence to coding standards.

Sprint Burndown

Sprint Burndown tracks the amount of work remaining in a sprint versus time for scrum teams. It helps development teams visualize progress and productivity throughout a sprint, helps identify potential issues early, and stay focused.

Operational Metrics

Operational Metrics are key performance indicators that provide insights into operational performance aspects, such as productivity, efficiency, and quality. They focus on the routine activities and processes that drive business operations and help to monitor, manage, and optimize operational performance. These metrics are: 

Incident Frequency

Incident Frequency tracks how often incidents or outages occur in a system or service. It helps to understand and mitigate disruptions in system operations. High Incident Frequency indicates frequent disruptions, while low incident frequency suggests a stable system but requires verification to ensure incidents aren’t underreported. 

Error Rate

Error Rate measures the frequency of errors occurring in the system, typically expressed as errors per transaction, request, or unit of time. It helps gauge system reliability and quality and highlights issues in performance or code that need addressing to improve overall stability.

Mean Time Between Failures (MTBF)

The mean Time between Failures tracks the average time between system failures. It signifies how often the failures are expected to occur in a given period. High MTBF indicates that the software is less reliable and needs less frequent maintenance. 

Security Metrics 

Security Metrics evaluate the effectiveness of an organization's security posture and its ability to protect information and systems from threats. They provide insights into understanding how well security measures function, identify vulnerabilities, and security control effectiveness. Key metrics are: 

Mean Time to Detect (MTTD) 

Mean Time to Detect tracks how long a team takes to detect threats. The longer the threat is unidentified, there is a high chance of an escalated problem. MTTD helps minimize the issues' impact in the early stages and refine monitoring and alert processes. 

Number of Vulnerabilities 

The number of Vulnerabilities measures the total vulnerabilities identified in the codebase. It assesses the system’s security posture, and remediation efforts and provides insights into the impact of security practices and tools. 

Mean Time to Patch

Mean Time to Patch reflects the time taken to fix security vulnerabilities, soft bugs, or other security issues. It assesses how quickly an organization can respond and manage vulnerabilities in the software delivery processes. 

Conclusion

Software development metrics play a vital role in aligning software development projects with business goals. These metrics help guide software engineers in making data-driven decisions and process improvements and ensure that projects progress smoothly, boost team performance, meet user needs, and drive overall success. Regularly analyzing these metrics optimizes development processes, manages technical debt, and ultimately delivers high-quality software to the end-users.

What are the Signs of Declining DORA Metrics?

Software development is an ever-evolving field that thrives on teamwork, collaboration, and productivity. Many organizations started shifting towards DORA metrics to measure their development processes as these metrics are like the golden standards of software delivery performance. 

But here’s the thing: Focusing solely on DORA Metrics isn’t just enough! Teams need to dig deep and uncover the root causes of any pesky issues affecting their metrics.

Enter the notorious world of underlying indicators! These troublesome signs point to deeper problems lurking in the development process that can drag down DORA metrics. Identifying and tackling these underlying issues helps to improve their development processes and, in turn, boost their DORA metrics.

In this blog post, we’ll dive into the uneasy relationship between these indicators and DORA Metrics, and how addressing them can help teams elevate their software delivery performance.

What are DORA Metrics?

Developed by the DevOps Research and Assessment team, DORA Metrics are key performance indicators that measure the effectiveness and efficiency of software development and delivery processes. With its data-driven approach, software teams can evaluate of the impact of operational practices on software delivery performance.

Four Key Metrics

  • Change Failure Rate measures the code quality released to production during software deployments.
  • Mean Time to Recover measures the time to recover a system or service after an incident or failure in production.

In 2021, the DORA Team added Reliability as a fifth metric. It is based upon how well the user’s expectations are met, such as availability and performance, and measures modern operational practices.

Signs leading to Poor DORA Metrics

Deployment Frequency

Deployment Frequency measures how often a team deploys code to production. Symptoms affecting this metric include:

  • High Rework Rate -  Frequent modifications to deployed code can delay future deployments as teams focus on fixing issues.
  • Oversized Pull Requests -  Large pull requests can complicate the review process, causing delays in deployment.
  • Manual Deployment Processes -  Reliance on manual steps can introduce errors and slow down the release cycle.
  • Poor Test Coverage -  Insufficient automated testing can lead to hesitancy in deploying changes, impacting frequency.
  • Low Team Morale -  Frustration from continuous issues can reduce motivation to deploy frequently.
  • Lack of Clear Objectives -: Unclear goals leads misalignment and wasted efforts which hinders deployment frequency.
  • Inefficient Branching Strategy -  A poorly designed branching strategy result in merge conflicts, integration issues, and delays in merging changes into the main branch which further impacts deployment frequency.
  • Inadequate Monitoring and Observability -  Lack of effective monitoring and observability tools can make it difficult to identify and troubleshoot issues in production. 

Lead Time for Changes 

Lead Time for Changes measures the time taken from code commit to deployment. Symptoms impacting this metric include:

  • High Technical Debt - Accumulated technical debt can complicate code changes, extending lead times.
  • Inconsistent Code Review Practices -  Variability in review quality can lead to delays in approval and testing.
  • High Cognitive Load -  Overloaded team members may struggle to focus, leading to slower progress on changes.
  • Frequent Context Switching - Team members shifting focus between tasks can increase lead time due to lost productivity.
  • Poor Communication -  Lack of collaboration can result in misunderstandings and delays in the development process.
  • Unclear Requirements -  Ambiguity in project requirements can lead to rework and extended lead times.
  • Inefficient Issue Tracking -  Poorly managed issue tracking systems can lead to lost or forgotten tasks, duplicated efforts, and delays in addressing issues, ultimately extending lead times.
  • Lack of Automated Testing -  Insufficient automated testing can lead to manual testing bottlenecks, delaying the integration and deployment of changes.

Change Failure Rate

Change Failure Rate indicates the percentage of changes that result in failures. Symptoms affecting this metric include:

  • Poor Test Coverage -  Insufficient testing increases the likelihood of bugs in production.
  • High Pull Request Revert Rate -  Frequent rollbacks suggest instability in the codebase, indicating a high change failure rate.
  • Lightning Pull Requests -  Rapid submissions without adequate review can introduce errors and increase failure rates
  • Inadequate Incident Response Procedures -  Poorly defined processes can lead to higher failure rates during deployments.
  • Knowledge Silos -  Lack of shared knowledge within the team can lead to mistakes and increased failure rates.
  • High Code Quality Bugs - Frequent bugs in the code can indicate underlying quality issues, raising the change failure rate.
  • Lack of Feature Flags -  The absence of feature flags can make it difficult to roll back changes or experiment with new features, increasing the risk of failures in production.
  • Insufficient Monitoring and Alerting - Inadequate monitoring and alerting systems can make it challenging to detect and respond to issues in production, leading to prolonged failures and increased change failure rates.

Mean Time to Restore Service

Mean Time to Restore Service measures how long it takes to recover from a failure. Symptoms impacting this metric include:

  • High Technical Debt -  Complexity in the codebase can slow down recovery efforts, extending MTTR.
  • Recurring High Cognitive Load -  Overburdened team members may take longer to diagnose and fix issues.
  • Poor Documentation -  Lack of clear documentation can hinder recovery efforts during incidents.
  • Inconsistent Incident Management -  Variability in handling incidents can lead to longer recovery times.
  • High Rate of Production Incidents -  Frequent issues can overwhelm the team, extending recovery times.
  • Lack of Post-Mortem Analysis -  Not analyzing incidents can prevent learning from failures, which can result in repeated issues and longer recovery times.
  • Insufficient Automation - Lack of automation in incident response and remediation processes causes manual, time-consuming troubleshooting, extending recovery times.
  • Inadequate Monitoring and Observability -  Insufficient monitoring and observability tools can make it difficult to quickly identify and diagnose issues in production which further delay the restoration of service.
  • Siloed Incident Response -  Lack of cross-functional collaboration and communication during incidents lead to delays in restoring service. As team members may not have a complete understanding of the issue or the necessary context to resolve it swiftly. 

Improve your DORA Metrics using Typo

Software analytics tools are an effective way to measure DORA DevOps metrics. These tools can automate data collection from various sources and provide valuable insights. They also offer centralized dashboards for easy visualization and analysis to identify bottlenecks and inefficiencies in the software delivery process. They also facilitate benchmarking against industry standards and previous performance to set realistic improvement goals. These software analytics tools promote collaboration between development and operations by providing a common framework for discussing performance. Hence, enhancing the ability to make data-driven decisions, drive continuous improvement, and improve customer satisfaction.

Typo is a powerful software engineering platform that enhances SDLC visibility, provides developer insights, and automates workflows to help you build better software faster. It integrates seamlessly with tools like GIT, issue trackers, and CI/CD systems. It offers a single dashboard with key DORA and other engineering metrics — providing comprehensive insights into your deployment process. Additionally, Typo includes engineering benchmarks for comparing your team's performance across industries.

Conclusion

DORA metrics are essential for evaluating software delivery performance, but they reveal only part of the picture. Addressing underlying issues affecting these metrics such as high deployment frequency or lengthy change lead time, can lead to significant improvements in software quality and team efficiency.

Use tools like Typo to gain deeper insights and benchmarks, enabling more effective performance enhancements.

Why SPACE Framework Matters?

The SPACE framework is a multidimensional approach to understanding and measuring developer productivity. Since the teams are increasingly distributed and users demand efficient and high-quality software, the SPACE framework provides a structured way to assess productivity beyond traditional metrics. 

In this blog post, we highlight the importance of the SPACE framework dimensions for software teams and explore its components, benefits, and practical applications.

Understanding the SPACE Framework

The SPACE framework is a multidimensional approach to measuring developer productivity. Below are five SPACE framework dimensions:

  • Satisfaction and Well-Being -  This dimension assesses whether the developers feel a sense of fulfillment in their roles and how the work environment impacts their mental health.
  • Performance - This focuses on developers’ performance based on the quality and impact of the work produced. 
  • Activity - This dimension tracks the actions and outputs of developers and further, providing insights into their workflow and engagement.
  • Communication and Collaboration -  This dimension measures how effectively team members collaborate with each other and have a clear understanding of their priorities.
  • Efficiency and Flow -  This dimension evaluates how smoothly the team’s work progresses with minimal interruptions and maximum productive time.

By examining these dimensions, the SPACE framework provides a comprehensive view of developer productivity that goes beyond traditional metrics.

Why the SPACE Framework Matters

The SPACE productivity framework is important for software development teams because it provides an in-depth understanding of productivity, significantly improving both team dynamics and software quality. Here are specific insights into how the SPACE framework benefits software teams:

Enhanced Developer Satisfaction and Retention

Focusing on satisfaction and well-being allows software engineering leaders to create a positive work environment. It is essential to retain top talent as developers who feel valued and supported are more likely to stay with the organization. 

Metrics such as employee satisfaction surveys and burnout assessments can highlight potential bottlenecks. For instance, if a team identifies low satisfaction scores, they can implement initiatives like team-building activities, flexible work hours, or mental health resources to increase morale.

Improved Performance Metrics

Emphasizing performance as an outcome rather than just output helps teams better align their work with business goals. This shift encourages developers to focus on delivering high-quality code that meets customer needs. 

Performance metrics might include customer satisfaction ratings, bug counts, and the impact of features on user engagement. For example, a team that measures the effectiveness of a new feature through user feedback can make informed decisions about future development efforts.

Data-Driven Activity Insights

The activity dimension provides valuable insights into how developers spend their time. Tracking various activities such as coding, code reviews, and collaboration helps in identifying bottlenecks and inefficiencies in their processes. 

For example, if a team notices that code reviews are taking too long, they can investigate the reasons behind the delays and implement strategies to streamline the review process, such as establishing clearer guidelines or increasing the number of reviewers.

Strengthened Communication and Collaboration

Effective communication and collaboration are crucial for successful software development. The SPACE framework fosters teams to assess their communication practices and identify potential bottlenecks. 

Metrics such as the speed of integrating work, the quality of peer reviews, and the discoverability of documentation reveal whether team members are able to collaborate well. Suppose, the team finds that onboarding new members takes too long. To improvise, they can enhance their documentation and mentorship programs to facilitate smoother transitions.

Optimized Efficiency and Flow

The efficiency and flow dimension focuses on minimizing interruptions and maximizing productive time. By identifying and addressing factors that disrupt workflow, teams can create an environment conducive to deep work. 

Metrics such as the number of interruptions, the time spent in value-adding activities, and the lead time for changes can help teams pinpoint inefficiencies. For example, a team may discover that frequent context switching between tasks is hindering productivity and can implement strategies like time blocking to improve focus.

Alignment with Organizational Goals

The SPACE framework promotes alignment between team efforts and organizational objectives. Measuring productivity in terms of business outcomes can ensure that their work contributes to overall success. 

For instance, if a team is tasked with improving user retention, they can focus their efforts on developing features that enhance the user experience. They can further measure their impact through relevant metrics.

Adaptability to Changing Work Environments

The rise of remote and hybrid models results in evolvement in the software development landscape. The SPACE framework offers the flexibility to adapt to new challenges. 

Teams can tailor their metrics to the unique dynamics of their work environment. So, they remain relevant and effective. For example, in a remote setting, teams might prioritize communication metrics so that collaboration remains strong despite physical distance.

Fostering a Culture of Continuous Improvement

Implementing the SPACE framework encourages a culture of continuous improvement within software development teams. Regularly reviewing productivity metrics and discussing them openly help to identify areas for growth and innovation. 

It fosters an environment where feedback is valued, team members feel heard and empowered to contribute to increasing productivity.

Reducing Misconceptions and Myths

The SPACE framework helps bust common myths about productivity, such as more activity equates to higher productivity. Providing a comprehensive view of productivity that includes satisfaction, performance, and collaboration can avoid the pitfalls of relying on simplistic metrics. Hence, fostering a more informed approach to productivity measurement and management.

Supporting Developer Well-Being

Ultimately, the SPACE framework recognizes that developer well-being is integral to productivity. By measuring satisfaction and well-being alongside performance and activity, teams can create a holistic view of productivity that prioritizes the health and happiness of developers. 

This focus on well-being not only enhances individual performance but also contributes to a positive team culture and overall organizational success.

Implementing the SPACE Framework in Practice

Implementing the SPACE framework effectively requires a structured approach. It blends the identification of relevant metrics, the establishment of baselines, and the continuous improvement culture. Here’s a detailed guide on how software teams can adopt the SPACE framework to enhance their productivity:

Define Clear Metrics for Each Dimension

To begin, teams must establish specific, actionable metrics for each of the five dimensions of the SPACE framework. This involves not only selecting metrics but also ensuring they are tailored to the team’s unique context and goals. Here are some examples for each dimension:

Satisfaction and Well-Being - 

  • Employee Satisfaction Surveys -  Regularly conduct surveys to measure developers' overall satisfaction with their work environment, tools, and team dynamics.
  • Burnout Assessments -  Implement tools to measure burnout levels including Maslach Burnout Inventory, to identify trends and areas for improvement. 

Performance

  • Quality Metrics -  Measure the number of bugs reported post-release, hotfixes frequency, and customer satisfaction scores related to specific features.
  • Impact Metrics -  Track the adoption rates of new features and the retention rates of users to assess the real-world impact of the development efforts.

Activity

  • Development Cycle Metrics -  Monitor the number of pull requests, commits, and code reviews completed within a sprint to understand activity levels.
  • Operational Metrics -  Track incidents and their resolution times to gauge the operational workload on developers.

Communication and Collaboration

  • Documentation Discoverability -  Track how quickly team members can find necessary documentation or expertise, through user feedback time-to-find metrics, or any other means. 
  • Integration Speed -  Track the time it takes for code to move from development to production. 

Efficiency and Flow

  • Flow Metrics -  Assess the average time developers spend in deep work vs. time spent on interruptions or in meetings.
  • Handoff Metrics -  Count the number of handoffs in the development process to identify potential delays or inefficiencies.

Establish Baselines and Set Goals

Once metrics are defined, teams should establish baselines for each metric. This involves collecting initial data to understand current performance levels. For example, a team measures the time taken for code reviews. They should gather data over several sprints to determine the average time before setting improvement goals.

Setting SMART (Specific, Measurable, Achievable, Relevant, Time-bound) goals based on these baselines enables teams to track progress effectively. For instance, if the average code review time is currently two days, a goal might be to reduce this to one day within the next quarter.

Foster Open Communication and Transparency

Foster a culture of open communication for the SPACE framework to be effective. Team members should feel comfortable discussing productivity metrics and sharing feedback. A few of the ways to do so include conducting regular team meetings where metrics are reviewed, challenges are addressed and successes are celebrated.

Encouraging transparency around metrics helps illustrate productivity measurements and ensures that all team members understand the rationale behind them. For instance, developers are aware that a high number of pull requests is not the sole indicator of productivity. This allows them to feel less pressure to increase activity without considering quality.

Regularly Review and Adapt Metrics

The SPACE framework's effectiveness relies on two factors: continuous evaluation and adaptation of the chosen metrics. Scheduling regular reviews (e.g., quarterly) allows to assess whether the metrics are providing meaningful insights and they need to be adjusted.

For example, a metric for measuring developer satisfaction reveals consistently low scores. Hence, the team should investigate the underlying causes and consider implementing changes, such as additional training or resources.

Integrate Metrics into Daily Workflows

To ensure that the SPACE framework is not just a theoretical exercise, teams should integrate the metrics into their daily workflows. This can be achieved through:

  • Dashboards -  Create visual dashboards that display real-time metrics for the team and allow developers to see their performance and areas for improvement at a glance.
  • Retrospectives -  Incorporate discussions around SPACE metrics into sprint retrospectives and allow teams to reflect on their productivity and identify actionable steps for the next sprint.
  • Recognition Programs -  Develop recognition programs that celebrate achievements related to the SPACE dimensions. For instance, acknowledging a team member who significantly improved code quality or facilitated effective collaboration can reinforce the importance of these metrics.

Encourage Continuous Learning and Improvement

Implementing the SPACE framework should be viewed as an ongoing journey rather than a one-time initiative. Encourage a culture of continuous learning where team members are motivated to seek out knowledge and improve their practices.

This can be facilitated through:

  • Workshops and Training -  Conduct sessions focused on best practices in coding, collaboration, and communication. This helps to improve skills that directly impact the metrics defined in the SPACE framework.
  • Mentorship Programs -  Pair experienced developers with newer team members for knowledge sharing and boosting overall team performance.

Leverage Technology Tools

Utilizing technology tools can streamline the implementation of the SPACE framework. Tools that facilitate project management, code reviews, and communication can provide valuable data for the defined metrics. For example:

  • Version Control Systems -  Tools like Git can help track activity metrics such as commits and pull requests.
  • Project Management Tools -  Platforms like Jira or Trello can assist in monitoring task completion rates and integration times.
  • Collaboration Tools -  Tools like Slack or Microsoft Teams can enhance communication and provide insights into team interactions.

Measure the Impact on Developer Well-Being

While the SPACE framework focuses on the importance of satisfaction and well-being, software teams should actively measure the impact of their initiatives on these dimensions. A few of the ways include follow-up surveys and feedback sessions after implementing changes. 

Suppose, a team introduces mental health days. They should assess whether this leads to increased satisfaction scores or reduced burnout levels in subsequent surveys.

Celebrate Successes and Learn from Failures

Recognizing and appreciating software developers helps to maintain morale and motivation within the team. The achievements should be acknowledged when teams achieve their goals related to the SPACE framework, including improved performance metrics or higher satisfaction scores. 

On the other hand, when challenges arise, teams should adopt a growth mindset and view failures as opportunities for learning and improvement. Conducting post-mortems on projects that did not meet expectations helps teams identify what went wrong and how to fix it in the future. 

Iterate and Evolve the Framework

Finally, the implementation of the SPACE productivity framework should be iterative. Teams gaining experience with the framework should continuously refine their approach based on feedback and results. It ensures that the framework remains relevant and effective in addressing the evolving needs of the development team and the organization.

How Typo Measure Metrics under SPACE Framework? 

Typo is a popular software engineering intelligence platform that offers SDLC visibility, developer insights, and workflow automation for building high-performing tech teams.

Here’s how Typo metrics fit into the SPACE framework's different dimensions: 

Satisfaction and Well-Being: With the Developer Experience feature, which includes focus and sub-focus areas, engineering leaders can monitor how developers feel about working at the organization, assess burnout risk, and identify necessary improvements. 

The automated code review tool auto-analyzes the codebase and pull requests to identify issues and auto-generate fixes before merging to master. This enhances satisfaction by ensuring quality and fostering collaboration.

Performance: The sprint analysis feature provides in-depth insights into the number of story points completed within a given time frame. It tracks and analyzes the team's progress throughout a sprint, showing the amount of work completed, work still in progress, and the remaining time. Typo’s code review tool understands the context of the code and quickly finds and fixes issues accurately. It also standardizes code, reducing the risk of security breaches and improving maintainability.

Activity: Typo measures developer activity through various metrics:

  • Number of Code Reviews: Indicates how often code reviews are performed.
  • Coding Time: Tracks the average time developers take to write and commit code changes.
  • Number of Commits: Shows development activity.
  • Lines of Code: Reflects the volume of code written.
  • Story Points Completed: Measures work completed against planned work.
  • Deployment Frequency: Tracks how often code is deployed into production each week.

Communication & Collaboration: Code coverage measures the percentage of the codebase tested by automated tests, while code reviews provide feedback on their effectiveness. PR Merge Time represents the average time taken from the approval of a Pull Request to its integration into the main codebase.

Efficiency and Flow: Typo assesses this dimension through two major metrics:

  • Code Review: Analyzes the codebase and pull requests to identify issues and auto-generate fixes before merging to master.
  • Velocity Metrics: It includes:
    • Cycle Time: The average time Pull Requests spend in different stages of the pipeline, including "Coding," "Pickup," "Review," and "Merge."
    • Coding Stage: The average time taken by developers to write and commit code changes.
    • Issue Cycle Time: The average time it takes for a ticket to move from the 'In Progress' state to the 'Completion' state.
    • Issue Velocity: The average number of completed tickets by a team within the selected period. 

By following the above-mentioned steps, dev teams can effectively implement the SPACE metrics framework to enhance productivity, improve developer satisfaction, and align their efforts with organizational goals. This structured approach not only encourages a healthier work culture but also drives better outcomes in software development.

Top 5 Pluralsight Flow Alternatives

Software development teams are key indicators of an organization’s success. With the ever-evolving tech landscape, it is crucial to understand how well they are performing and what needs to be fixed. 

This is how software development analytics tools came to the rescue. These tools provide insights into various metrics related to the development workflow, measure progress, and make informed decisions. 

One such tool is Pluralsight Flow which is popular in development teams nowadays. But, will it work for you? Maybe. Maybe not! 

So, worry not! We have curated the top 5 Pluralsight Flow alternatives that you can take note of when considering software development analytics tools for your organization. 

What is Pluralsight Flow?

Pluralsight Flow, a leading engineering analytics platform, aggregates GIT data into comprehensive insights. It gathers important engineering metrics such as DORA metrics code commits and pull requests, all displayed in a centralized dashboard. It can be integrated with manual and automated testing such as Azure DevOps and GitLab. 

However, there is a lack of support for certain programming languages and the cost is considered high, with no free trial available. 

Pluralsight Flow Alternatives 

Given the downsides of Pluralsight Flow, there are other top alternatives available in the market. Take a look below: 

Typo

Typo is an AI-driven engineering analytics platform that offers SDLC visibility, data-driven insights, and workflow automation for software teams. It provides comprehensive insights through DORA and other engineering metrics in a centralized dashboard. Typo’s pre-built integration in the dev tool stack helps to highlight developer blockers, predict sprint delays, and measure business impact. Its automated code tool allows engineering teams to identify issues and auto-fix them before merging to master. Typo’s holistic framework captures developer experience to understand what causes friction and ways to improve. 

Price:

  • Free: $0/dev/month
  • Starter: $16/dev/month
  • Pro: $24/dev/month
  • Enterprise: Quotation on request

LinearB

LinearB is a real-time performance analysis tool that measures Git data, tracks DORA metrics, and collects data from other tools. It highlights automatable tasks to software teams, helping them save time and resources. Its project delivery forecast allows the team to stay on schedule and communicate project delivery status updates. It can also be integrated with third-party applications such as Jira, Slack, Shortcut, and other popular tools. 

Price:

  • Free: $0/dev/month
  • Business: $49/dev/month
  • Enterprise: Quotation on request

Jellyfish 

Jellyfish is an engineering management platform that aligns software engineering insights with company goals. It translates tech data into reports and insights for management and leadership to understand easily. It provides real-time visibility into engineering work quickly and allows the team members to track key metrics such as PR statuses, code commits, and overall project progress. Jellyfish offers multiple perspectives on resource allocation and helps to track investments made during product development. 

Jellyfish Software Reviews, Demo & Pricing - 2024

Price

  • Quotation on request

Waydev

Waydev is an analytics intelligence platform that uses the agile method to track the software development team output. It primarily focuses on market-based metrics and allows engineering leaders to see data from different perspectives. It offers tailored reports that help in aligning engineering output with business goals. Waydev also gives the cost and progress of delivery and key initiatives. It can be seamlessly integrated with third-party tools including Gitlab, Github, and CircleCI. 

Price

  • Quotation on request

Swarmia 

Swarmia is an engineering effectiveness platform that offers visibility into three key areas: business outcome, developer productivity, and developer experience. Its automation feature allows all tasks to be assigned to the appropriate issues and concerned person. It also has a working agreement feature that helps to set numerical targets for activities like managing open pull requests and code reviews. 

Initiatives | Swarmia

Price

  • Free: 0£/dev/month
  • Lite: 20£/dev/month
  • Standard: 39£/dev/month

Conclusion 

While we have shared top software development analytics tools, don’t forget to conduct thorough research before selecting for your engineering team. Check whether it aligns well with your requirements, facilitates team collaboration and continuous improvement, integrates seamlessly with your existing and upcoming tools, and so on. 

All the best! 

4 Key DevOps Metrics for Improved Performance

Lots of organizations are prioritizing the adoption and enhancement of their DevOps practices. The aim is to optimize the software development life cycle and increase delivery speed which enables faster market reach and improved customer service. 

In this article, we’ve shared four key DevOps metrics, their importance and other metrics to consider. 

What are DevOps Metrics?

DevOps metrics are the key indicators that showcase the performance of the DevOps software development pipeline. By bridging the gap between development and operations, these metrics are essential for measuring and optimizing the efficiency of both processes and people involved.

Tracking DevOps metrics allows teams to quickly identify and eliminate bottlenecks, streamline workflows, and ensure alignment with business objectives.

Four Key DevOps Metrics 

Here are four important DevOps metrics to consider:

Deployment Frequency 

Deployment Frequency measures how often code is deployed into production per week, taking into account everything from bug fixes and capability improvements to new features. It is a key indicator of agility, and efficiency and a catalyst for continuous delivery and iterative development practices that align seamlessly with the principles of DevOps. A wrong approach in the first key metric can degrade the other DORA metrics.

Deployment Frequency is measured by dividing the number of deployments made during a given period by the total number of weeks/days. One deployment per week is standard. However, it also depends on the type of product.

Importance of High Deployment Frequency

  • High deployment frequency allows new features, improvements, and fixes to reach users more rapidly. It allows companies to quickly respond to market changes, customer feedback, and emerging trends.
  • Frequent deployments usually involve incremental, manageable changes, which are easier to test, debug, and validate. Moreover, It helps to identify and address bugs and issues more quickly, reducing the risk of significant defects in production.
  • High deployment frequency leads to higher satisfaction and loyalty as it allows continuous improvement and timely resolution of issues. Moreover, users get access to new features and enhancements without long waits which improves their overall experience.
  • Deploying smaller changes reduces the risk associated with each deployment, making rollbacks and fixes simpler. Moreover, continuous integration and deployment provide immediate feedback, allowing teams to address problems before they escalate.
  • Regular, automated deployments reduce the stress and fear often associated with infrequent, large-scale releases. Development teams can iterate on their work more quickly, which leads to faster innovation and problem-solving.

Lead Time for Changes

Lead Time for Changes measures the time it takes for a code change to go through the entire development pipeline and become part of the final product. It is a critical metric for tracking the efficiency and speed of software delivery. The measurement of this metric offers valuable insights into the effectiveness of development processes, deployment pipelines, and release strategies.

To measure this metric, DevOps should have:

  • The exact time of the commit 
  • The number of commits within a particular period
  • The exact time of the deployment 

Divide the total sum of time spent from commitment to deployment by the number of commitments made.

Importance of Reduced Lead Time for Changes

  • Short lead times allow new features and improvements to reach users quickly, delivering immediate value and outpacing competitors by responding to market needs and trends timely. 
  • Customers see their feedback addressed promptly, which leads to higher satisfaction and loyalty. Bugs and issues can be fixed and deployed rapidly which improves user experience. 
  • Developers spend less time waiting for deployments and more time on productive work which reduces context switching. It also enables continuous improvement and innovation which keeps the development process dynamic and effective.
  • Reduced lead time encourages experimentation. This allows businesses to test new ideas and features rapidly and pivot quickly in response to market changes, regulatory requirements, or new opportunities.
  • Short lead times help in better allocation and utilization of resources. It helps to avoid prolonged delays and smoother operations. 

Change Failure Rate

Change Failure Rate refers to the proportion or percentage of deployments that result in failure or errors, indicating the rate at which changes negatively impact the stability or functionality of the system. It reflects the stability and reliability of the entire software development and deployment lifecycle. Tracking CFR helps identify bottlenecks, flaws, or vulnerabilities in processes, tools, or infrastructure that can negatively impact the quality, speed, and cost of software delivery.

To calculate CFR, follow these steps:

  • Identify Failed Changes: Keep track of the number of changes that resulted in failures during a specific timeframe.
  • Determine Total Changes Implemented: Count the total changes or deployments made during the same period.

Apply the formula:

Use the formula CFR = (Number of Failed Changes / Total Number of Changes) * 100 to calculate the Change Failure Rate as a percentage.

Importance of Low Change Failure Rate

  • Low change failure rates ensure the system remains stable and reliable which leads to lower downtime and disruptions. Moreover, consistent reliability builds trust with users. 
  • Reliable software increases customer satisfaction and loyalty, as users can depend on the product for their needs. This further lowers issues and interruptions, leading to a more seamless and satisfying experience.
  • Reduced change failure rates result in reliable and efficient software which leads to higher customer retention and positive word-of-mouth referrals. It can also provide a competitive edge in the market that attracts and retains customers.
  • Fewer failures translate to lower costs that are associated with diagnosing and fixing issues in production. This also allows resources to be better allocated to development and innovation rather than maintenance and support.
  • Low failure rates contribute to a more positive and motivated work environment. It further gives teams confidence in their deployment processes and the quality of their code. 

Mean Time to Restore

Mean Time to Restore (MTTR) represents the average time taken to resolve a production failure/incident and restore normal system functionality each week. Measuring "Mean Time to Restore" (MTTR) provides crucial insights into an engineering team's incident response and resolution capabilities. It helps identify areas of improvement, optimize processes, and enhance overall team efficiency. 

To calculate this, add the total downtime and divide it by the total number of incidents that occurred within a particular period.

Importance of Reduced Mean Time to Restore

  • Reduced MTTR minimizes system downtime i.e. higher availability of services and systems, which is critical for maintaining user trust and satisfaction.
  • Faster recovery from incidents means that users experience less disruption. This leads to higher customer satisfaction and loyalty, especially in competitive markets where service reliability can be a key differentiator.
  • Frequent or prolonged downtimes can damage a company’s reputation. Quick restoration times help maintain a good reputation by demonstrating reliability and a strong capacity for issue resolution.
  • Keeping MTTR low helps in meeting these SLAs, avoiding penalties, and maintaining good relationships with clients and stakeholders.
  • Reduced MTTR encourages a proactive culture of monitoring, alerting, and preventive maintenance. This can lead to identifying and addressing potential issues swiftly, which further enhances system reliability.

Other DevOps Metrics to Consider 

Apart from the above-mentioned key metrics, there are other metrics to take into account. These are: 

Cycle Time 

Cycle time measures the total elapsed time taken to complete a specific task or work item from the beginning to the end of the process.

Mean Time to Failure 

Mean Time to Failure (MTTF) is a reliability metric used to measure the average time a non-repairable system or component operates before it fails.

Error Rates

Error Rates measure the number of errors encountered in the platform. It identifies the stability, reliability, and user experience of the platform.

Response Time

Response time is the total time from when a user makes a request to when the system completes the action and returns a result to the user.

How Typo Leverages DevOps Metrics? 

Typo is a powerful tool designed specifically for tracking and analyzing DORA metrics. It provides an efficient solution for development teams seeking precision in their DevOps performance measurement.

  • With pre-built integrations in the dev tool stack, the DORA metrics dashboard provides all the relevant data within minutes.
  • It helps in deep diving and correlating different metrics to identify real-time bottlenecks, sprint delays, blocked PRs, deployment efficiency, and much more from a single dashboard.
  • The dashboard sets custom improvement goals for each team and tracks their success in real time.
  • It gives real-time visibility into a team’s KPI and lets them make informed decisions.

Conclusion

Adopting and enhancing DevOps practices is essential for organizations that aim to optimize their software development lifecycle. Tracking these DevOps metrics helps teams identify bottlenecks, improve efficiency, and deliver high-quality products faster. 

How to Improve Software Delivery Using DORA Metrics

In today's software development landscape, effective collaboration among teams and seamless service orchestration are essential. Achieving these goals requires adherence to organizational standards for quality, security, and compliance. Without diligent monitoring, organizations risk losing sight of their delivery workflows, complicating the assessment of impacts on release velocity, stability, developer experience, and overall application performance.

To address these challenges, many organizations have begun tracking DevOps Research and Assessment (DORA) metrics. These metrics provide crucial insights for any team involved in software development, offering a comprehensive view of the Software Development Life Cycle (SDLC). DORA metrics are particularly useful for teams practising DevOps methodologies, including Continuous Integration/Continuous Deployment (CI/CD) and Site Reliability Engineering (SRE), which focus on enhancing system reliability.

However, the collection and analysis of these metrics can be complex. Decisions about which data points to track and how to gather them often fall to individual team leaders. Additionally, turning this data into actionable insights for engineering teams and leadership can be challenging. 

Understanding DORA DevOps Metrics

The DORA research team at Google conducts annual surveys of IT professionals to gather insights into industry-wide software delivery practices. From these surveys, four key metrics have emerged as indicators of software teams' performance, particularly regarding the speed and reliability of software deployment. These key DORA metrics include:

DORA metrics connect production-based metrics with development-based metrics, providing quantitative measures that complement qualitative insights into engineering performance. They focus on two primary aspects: speed and stability. Deployment frequency and lead time for changes relate to throughput, while time to restore services and change failure rate address stability.

Contrary to the historical view that speed and stability are opposing forces, research from DORA indicates a strong correlation between these metrics in terms of overall performance. Additionally, these metrics often correlate with key indicators of system success, such as availability, thus offering insights that benefit application performance, reliability, delivery workflows, and developer experience.

Collecting and Analyzing DORA Metrics

While DORA DevOps metrics may seem straightforward, measuring them can involve ambiguity, leading teams to make challenging decisions about which data points to use. Below are guidelines and best practices to ensure accurate and actionable DORA metrics.

Defining the Scope

Establishing a standardized process for monitoring DORA metrics can be complicated due to differing internal procedures and tools across teams. Clearly defining the scope of your analysis—whether for a specific department or a particular aspect of the delivery process—can simplify this effort. It’s essential to consider the type and amount of work involved in different analyses and standardize data points to align with team, departmental, or organizational goals.

For example, platform engineering teams focused on improving delivery workflows may prioritize metrics like deployment frequency and lead time for changes. In contrast, SRE teams focused on application stability might prioritize change failure rate and time to restore service. By scoping metrics to specific repositories, services, and teams, organizations can gain detailed insights that help prioritize impactful changes.

Best Practices for Defining Scope:

  • Engage Stakeholders: Involve stakeholders from various teams (development, QA, operations) to understand their specific needs and objectives.
  • Set Clear Goals: Establish clear goals for what you aim to achieve with DORA metrics, such as improving deployment frequency or reducing change failure rates.
  • Prioritize Based on Objectives: Depending on your team's goals, prioritize metrics accordingly. For example, teams focused on enhancing deployment speed should emphasize deployment frequency and lead time for changes.
  • Standardize Definitions: Create standardized definitions for metrics across teams to ensure consistency in data collection and analysis.

Standardizing Data Collection

To maintain consistency in collecting DORA metrics, address the following questions:

1. What constitutes a successful deployment?

Establish clear criteria for what defines a successful deployment within your organization. Consider the different standards various teams might have regarding deployment stages. For instance, at what point do you consider a progressive release to be "executed"?

2. What defines a failure or response?

Clarify definitions for system failures and incidents to ensure consistency in measuring change failure rates. Differentiate between incidents and failures based on factors such as application performance and service level objectives (SLOs). For example, consider whether to exclude infrastructure-related issues from DORA metrics.

3. When does an incident begin and end?

Determine relevant data points for measuring the start and resolution of incidents, which are critical for calculating time to restore services. Decide whether to measure from when an issue is detected, when an incident is created, or when a fix is deployed.

4. What time spans should be used for analysis?

Select appropriate time frames for analyzing data, taking into account factors like organization size, the age of the technology stack, delivery methodology, and key performance indicators (KPIs). Adjust time spans to align with the frequency of deployments to ensure realistic and comprehensive metrics.

Best Practices for Standardizing Data Collection:

  • Develop Clear Guidelines: Establish clear guidelines and definitions for each metric to minimize ambiguity.
  • Automate Data Collection: Implement automation tools to ensure consistent data collection across teams, thereby reducing human error.
  • Conduct Regular Reviews: Regularly review and update definitions and guidelines to keep them relevant and accurate.

Utilizing DORA Metrics to Enhance CI/CD Workflows

Establishing a Baseline

Before diving into improvements, it’s crucial to establish a baseline for your current continuous integration and continuous delivery performance using DORA metrics. This involves gathering historical data to understand where your organization stands in terms of deployment frequency, lead time, change failure rate, and MTTR. This baseline will serve as a reference point to measure the impact of any changes you implement.

Analyzing Deployment Frequency

Actionable Insights: If your deployment frequency is low, it may indicate issues with your CI/CD pipeline or development process. Investigate potential causes, such as manual steps in deployment, inefficient testing procedures, or coordination issues among team members.

Strategies for Improvement:

  • Automate Testing and Deployment: Implement automated testing frameworks that allow for continuous integration, enabling more frequent and reliable deployments.
  • Adopt Feature Toggles: This technique allows teams to deploy code without exposing it to users immediately, increasing deployment frequency without compromising stability.

Reducing Lead Time for Changes

Actionable Insights: Long change lead time often points to inefficiencies in the development process. By analyzing your CI/CD pipeline, you can identify delays caused by manual approval processes, inadequate testing, or other obstacles.

Strategies for Improvement:

  • Streamline Code Reviews: Establish clear guidelines and practices for code reviews to minimize bottlenecks.
  • Use Branching Strategies: Adopt effective branching strategies (like trunk-based development) that promote smaller, incremental changes, making the integration process smoother.

Lowering Change Failure Rate

Actionable Insights: A high change failure rate is a clear sign that the quality of code changes needs improvement. This can be due to inadequate testing or rushed deployments.

Strategies for Improvement:

  • Enhance Testing Practices: Implement comprehensive automated tests, including unit, integration, and end-to-end tests, to ensure quality before deployment.
  • Conduct Post-Mortems: Analyze failures to identify root causes and learn from them. Use this knowledge to adjust processes and prevent similar issues in the future.

Improving Mean Time to Recover (MTTR)

Actionable Insights: If your MTTR is high, it suggests challenges in incident management and response capabilities. This can lead to longer downtimes and reduced user trust.

Strategies for Improvement:

  • Invest in Monitoring and Observability: Implement robust monitoring tools to quickly detect and diagnose issues, allowing for rapid recovery.
  • Create Runbooks: Develop detailed runbooks that outline recovery procedures for common incidents, enabling your team to respond quickly and effectively.

Continuous Improvement Cycle

Utilizing DORA metrics is not a one-time activity but part of an ongoing process of continuous improvement. Establish a regular review cycle where teams assess their DORA metrics and adjust practices accordingly. This creates a culture of accountability and encourages teams to seek out ways to improve their CI/CD workflows continually.

Case Studies: Real-World Applications

1. Etsy

Etsy, an online marketplace, adopted DORA metrics to assess and enhance its CI/CD workflows. By focusing on improving its deployment frequency and lead time for changes, Etsy was able to increase deployment frequency from once a week to multiple times a day, significantly improving responsiveness to customer needs.

2. Flickr

Flickr used DORA metrics to track its change failure rate. By implementing rigorous automated testing and post-mortem analysis, Flickr reduced its change failure rate significantly, leading to a more stable production environment.

3. Google

Google's Site Reliability Engineering (SRE) teams utilize DORA metrics to inform their practices. By focusing on MTTR, Google has established an industry-leading incident response culture, resulting in rapid recovery from outages and high service reliability.

Leveraging Typo for Monitoring DORA Metrics

Typo is a powerful tool designed specifically for tracking and analyzing DORA metrics. It provides an efficient solution for development teams seeking precision in their DevOps performance measurement.

  • With pre-built integrations in the dev tool stack, the DORA metrics dashboard provides all the relevant data within minutes.
  • It helps in deep diving and correlating different metrics to identify real-time bottlenecks, sprint delays, blocked PRs, deployment efficiency, and much more from a single dashboard.
  • The dashboard sets custom improvement goals for each team and tracks their success in real time.
  • It gives real-time visibility into a team’s KPI and lets them make informed decisions.

Importance of DORA Metrics for Boosting Tech Team Performance

DORA metrics serve as a compass for engineering teams, optimizing development and operations processes to enhance efficiency, reliability, and continuous improvement in software delivery.

In this blog, we explore how DORA metrics boost tech team performance by providing critical insights into software development and delivery processes.

What are DORA Metrics?

DORA metrics, developed by the DevOps Research and Assessment team, are a set of key performance indicators that measure the effectiveness and efficiency of software development and delivery processes. They provide a data-driven approach to evaluate the impact of operational practices on software delivery performance.

Four Key DORA Metrics

  • Deployment Frequency: It measures how often code is deployed into production per week. 
  • Lead Time for Changes: It measures the time it takes for code changes to move from inception to deployment. 
  • Change Failure Rate: It measures the code quality released to production during software deployments.
  • Mean Time to Recover: It measures the time to recover a system or service after an incident or failure in production.

In 2021, the DORA Team added Reliability as a fifth metric. It is based upon how well the user’s expectations are met, such as availability and performance, and measures modern operational practices.

How do DORA Metrics Drive Performance Improvement for Tech Teams? 

Here’s how key DORA metrics help in boosting performance for tech teams: 

Deployment Frequency 

Deployment Frequency is used to track the rate of change in software development and to highlight potential areas for improvement. A wrong approach in the first key metric can degrade the other DORA metrics.

One deployment per week is standard. However, it also depends on the type of product.

How does it Drive Performance Improvement? 

  • Frequent deployments allow development teams to deliver new features and updates to end-users quickly. Hence, enabling them to respond to market demands and feedback promptly.
  • Regular deployments make changes smaller and more manageable. Hence, reducing the risk of errors and making identifying and fixing issues easier. 
  • Frequent releases offer continuous feedback on the software’s performance and quality. This facilitates continuous improvement and innovation.

Lead Time for Changes

Lead Time for Changes is a critical metric used to measure the efficiency and speed of software delivery. It is the duration between a code change being committed and its successful deployment to end-users. 

The standard for Lead time for Change is less than one day for elite performers and between one day and one week for high performers.

How does it Drive Performance Improvement? 

  • Shorter lead times indicate that new features and bug fixes reach customers faster. Therefore, enhancing customer satisfaction and competitive advantage.
  • Reducing lead time highlights inefficiencies in the development process, which further prompts software teams to streamline workflows and eliminate bottlenecks.
  • A shorter lead time allows teams to quickly address critical issues and adapt to changes in requirements or market conditions.

Change Failure Rate

CFR, or Change Failure Rate measures the frequency at which newly deployed changes lead to failures, glitches, or unexpected outcomes in the IT environment.

0% - 15% CFR is considered to be a good indicator of code quality.

How does it Drive Performance Improvement? 

  • A lower change failure rate highlights higher quality changes and a more stable production environment.
  • Measuring this metric helps teams identify bottlenecks in their development process and improve testing and validation practices.
  • Reducing the change failure rate enhances the confidence of both the development team and stakeholders in the reliability of deployments.

Mean Time to Recover 

MTTR, which stands for Mean Time to Recover, is a valuable metric that provides crucial insights into an engineering team's incident response and resolution capabilities.

Less than one hour is considered to be a standard for teams.  

How does it Drive Performance Improvement? 

  • Reducing MTTR boosts the overall resilience of the system. Hence, ensuring that services are restored quickly and minimizing downtime.
  • Users experience less disruption due to quick recovery from failures. This helps in maintaining customer trust and satisfaction. 
  • Tracking MTTR advocates teams to analyze failures, learn from incidents, and implement preventative measures to avoid similar issues in the future.

How to Implement DORA Metrics in Tech Teams? 

Collect the DORA Metrics 

Firstly, you need to collect DORA Metrics effectively. This can be done by integrating tools and systems to gather data on key DORA metrics. There are various DORA metrics trackers in the market that make it easier for development teams to automatically get visual insights in a single dashboard. The aim is to collect the data consistently over time to establish trends and benchmarks. 

Analyze the DORA Metrics 

The next step is to analyze them to understand your development team's performance. Start by comparing metrics to the DORA benchmarks to see if the team is an Elite, High, Medium, or Low performer. Ensure to look at the metrics holistically as improvements in one area may come at the expense of another. So, always strive for balanced improvements. Regularly review the collected metrics to identify areas that need the most improvement and prioritize them first. Don’t forget to track the metrics over time to see if the improvement efforts are working.

Drive Improvements and Foster a DevOps Culture 

Leverage the DORA metrics to drive continuous improvement in engineering practices. Discuss what’s working and what’s not and set goals to improve metric scores over time. Don’t use DORA metrics on a sole basis. Tie it with other engineering metrics to measure it holistically and experiment with changes to tools, processes, and culture. 

Encourage practices like: 

  • Implement small changes and measure their impact.
  • Share the DORA metrics transparently with the team to foster a culture of continuous improvement.
  • Promote cross-collaboration between development and operations teams.
  • Focus on learning from failures rather than assigning blame.

Typo - A Leading DORA Metrics Tracker 

Typo is a powerful tool designed specifically for tracking and analyzing DORA metrics, providing an alternative and efficient solution for development teams seeking precision in their DevOps performance measurement.

  • With pre-built integrations in the dev tool stack, the DORA dashboard provides all the relevant data flowing in within minutes.
  • It helps in deep diving and correlating different metrics to identify real-time bottlenecks, sprint delays, blocked PRs, deployment efficiency, and much more from a single dashboard.
  • The dashboard sets custom improvement goals for each team and tracks their success in real time.
  • It gives real-time visibility into a team’s KPI and lets them make informed decisions.

Conclusion

DORA metrics are not just metrics; they are strategic navigators guiding tech teams toward optimized software delivery. By focusing on key DORA metrics, tech teams can pinpoint bottlenecks and drive sustainable performance enhancements. 

The Fifth DORA Metric: Reliability

The DORA (DevOps Research and Assessment) metrics have emerged as a north star for assessing software delivery performance.  The fifth metric, Reliability is often overlooked as it was added after the original announcement of the DORA research team. 

In this blog, let’s explore Reliability and its importance for software development teams. 

What are DORA Metrics? 

DevOps Research and Assessment (DORA) metrics are a compass for engineering teams striving to optimize their development and operations processes.

In 2015, The DORA (DevOps Research and Assessment) team was founded by Gene Kim, Jez Humble, and Dr. Nicole Forsgren to evaluate and improve software development practices. The aim is to enhance the understanding of how development teams can deliver software faster, more reliably, and of higher quality.

Four key metrics are: 

  • Deployment Frequency: Deployment frequency measures the rate of change in software development and highlights potential bottlenecks. It is a key indicator of agility and efficiency. Regular deployments signify a streamlined pipeline, allowing teams to deliver features and updates faster.
  • Lead Time for Changes: Lead Time for Changes measures the time it takes for code changes to move from inception to deployment. It tracks the speed and efficiency of software delivery and offers valuable insights into the effectiveness of development processes, deployment pipelines, and release strategies.
  • Change Failure Rate: Change failure rate measures the frequency at which newly deployed changes lead to failures, glitches, or unexpected outcomes in the IT environment. It reflects the reliability and efficiency and is related to team capacity, code complexity, and process efficiency, impacting speed and quality.
  • Mean Time to Recover: Mean Time to Recover measures the average duration taken by a system or application to recover from a failure or incident. It concentrates on determining the efficiency and effectiveness of an organization's incident response and resolution procedures.

What is Reliability?

Reliability is a fifth metric that was added by the DORA team in 2021. It is based upon how well your user’s expectations are met, such as availability and performance, and measures modern operational practices. It doesn’t have standard quantifiable targets for performance levels rather it depends upon service level indicators or service level objectives. 

While the first four DORA metrics (Deployment Frequency, Lead Time for Changes, Change Failure Rate, and Mean Time to Recover) target speed and efficiency, reliability focuses on system health, production readiness, and stability for delivering software products.  

Reliability comprises various metrics used to assess operational performance including availability, latency, performance, and scalability that measure user-facing behavior, software SLAs, performance targets, and error budgets. It has a substantial impact on customer retention and success. 

Indicators to Follow when Measuring Reliability

A few indicators include:

  • Availability: How long the software was available without incurring any downtime.
  • Error Rates: Number of times software fails or produces incorrect results in a given period. 
  • Mean Time Between Failures (MTBF): The average time passes between software breakdowns or failures. 
  • Mean Time to Recover (MTTR): The average time it takes for the software to recover from a failure. 

These metrics provide a holistic view of software reliability by measuring different aspects such as failure frequency, downtime, and the ability to quickly restore service. Tracking these few indicators can help identify reliability issues, meet service level agreements, and enhance the software’s overall quality and stability. 

Impact of Reliability on Overall DevOps Performance 

The fifth DevOps metric, Reliability, significantly impacts overall performance. Here are a few ways: 

Enhances Customer Experience

Tracking reliability metrics like uptime, error rates, and mean time to recovery allows DevOps teams to proactively identify and address issues. Therefore, ensuring a positive customer experience and meeting their expectations. 

Increases Operational Efficiency

Automating monitoring, incident response, and recovery processes helps DevOps teams to focus more on innovation and delivering new features rather than firefighting. This boosts overall operational efficiency.

Better Team Collaboration

Reliability metrics promote a culture of continuous learning and improvement. This breaks down silos between development and operations, fostering better collaboration across the entire DevOps organization.

Reduces Costs

Reliable systems experience fewer failures and less downtime, translating to lower costs for incident response, lost productivity, and customer churn. Investing in reliability metrics pays off through overall cost savings. 

Fosters Continuous Improvement

Reliability metrics offer valuable insights into system performance and bottlenecks. Continuously monitoring these metrics can help identify patterns and root causes of failures, leading to more informed decision-making and continuous improvement efforts.

Role of Reliability in Distinguishing Elite Performers from Low Performers

Importance of Reliability for Elite Performers

  • Reliability provides a more holistic view of software delivery performance. Besides capturing velocity and stability, it also takes the ability to consistently deliver reliable services to users into consideration. 
  • Elite-performing teams deploy quickly with high stability and also demonstrate strong operational reliability. They can quickly detect and resolve incidents, minimizing disruptions to the user experience.
  • Low-performing teams may struggle with reliability. This leads to more frequent incidents, longer recovery times, and overall less reliable service for customers.

Distinguishing Elite from Low Performers

  • Elite teams excel across all five DORA Metrics. 
  • Low performers may have acceptable velocity metrics but struggle with stability and reliability. This results in more incidents, longer recovery times, and an overall less reliable service.
  • The reliability metric helps identify teams that have mastered both the development and operational aspects of software delivery. 

Conclusion 

The reliability metric with the other four DORA DevOps metrics offers a more comprehensive evaluation of software delivery performance. By focusing on system health, stability, and the ability to meet user expectations, this metric provides valuable insights into operational practices and their impact on customer satisfaction. 

Implementing DORA DevOps Metrics in Large Organizations

Introduction

In software engineering, aligning your work with business goals is crucial. For startups, this is often straightforward. Small teams work closely together, and objectives are tightly aligned. However, in large enterprises where multiple teams are working on different products with varied timelines, this alignment becomes much more complex. In these scenarios, effective communication with leadership and establishing standard metrics to assess engineering performance is key. DORA Metrics is a set of key performance indicators that help organizations measure and improve their software delivery performance.

But first, let’s understand in brief how engineering works in startups vs. large enterprises -

Software Engineering in Startups: A Focused Approach

In startups, small, cross-functional teams work towards a single goal: rapidly developing and delivering a product that meets market needs. The proximity to business objectives is close, and the feedback loop is short. Decision-making is quick, and pivoting based on customer feedback is common. Here, the primary focus is on speed and innovation, with less emphasis on process and documentation.

Success in a startup's engineering efforts can often be measured by a few key metrics: time-to-market, user acquisition rates, and customer satisfaction. These metrics directly reflect the company's ability to achieve its business goals. This simple approach allows for quick adjustments and real-time alignment of engineering efforts with business objectives.

Engineering Goals in Large Enterprises: A Complex Landscape

Large enterprises operate in a vastly different environment. Multiple teams work on various products, each with its own roadmap, release schedules, and dependencies. The scale and complexity of operations require a structured approach to ensure that all teams align with broader organizational goals.

In such settings, communication between teams and leadership becomes more formalized, and standard metrics to assess performance and progress are critical. Unlike startups, where the impact of engineering efforts is immediately visible, large enterprises need a consolidated view of various performance indicators to understand how engineering work contributes to business objectives.

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The Challenge of Communication and Metrics in Large Organizations

Effective communication in large organizations involves not just sharing information but ensuring that it's understood and acted upon across all levels. Engineering teams must communicate their progress, challenges, and needs to leadership in a manner that is both comprehensive and actionable. This requires a common language of metrics that can accurately represent the state of development efforts.

Standard metrics are essential for providing this common language. They offer a way to objectively assess the performance of engineering teams, identify areas for improvement, and make informed decisions. However, the selection of these metrics is crucial. They must be relevant, actionable, and aligned with business goals.

Introducing DORA Metrics

DORA Metrics, developed by the DevOps Research and Assessment team, provide a robust framework for measuring the performance and efficiency of software delivery in DevOps and platform engineering. These metrics focus on key aspects of software development and delivery that directly impact business outcomes.

The four key DORA Metrics are:

These metrics provide a comprehensive view of the software delivery pipeline, from development to deployment and operational stability. By focusing on these key areas, organizations can drive improvements in their DevOps practices and enhance overall developer efficiency.

Using DORA Metrics in DevOps and Platform Engineering

In large enterprises, the application of DORA DevOps Metrics can significantly improve developer efficiency and software delivery processes. Here’s how these key DORA metrics can be used effectively:

  1. Deployment Frequency: It is a key indicator of agility and efficiency.some text
    • Goal: Increase the frequency of deployments to ensure that new features and fixes are delivered to customers quickly.
    • Action: Encourage practices such as Continuous Integration and Continuous Deployment (CI/CD) to automate the build and release process. Monitor deployment frequency across teams to identify bottlenecks and areas for improvement.
  2. Lead Time for Changes: It tracks the speed and efficiency of software delivery. some textsome text
    • Goal: Reduce the time it takes for changes to go from commit to production.
    • Action: Streamline the development pipeline by automating testing, reducing manual interventions, and optimizing code review processes. Use tools that provide visibility into the pipeline to identify delays and optimize workflows.
  3. Mean Time to Recover (MTTR): It concentrates on determining efficiency and effectiveness. some textsome text
    • Goal: Minimize downtime when incidents occur to ensure high availability and reliability of services.
    • Action: Implement robust monitoring and alerting systems to quickly detect and diagnose issues. Foster a culture of incident response and post-mortem analysis to continuously improve response times.
  4. Change Failure Rate: It reflects reliability and efficiency. some textsome text
    • Goal: Reduce the percentage of changes that fail in production to ensure a stable and reliable release process.
    • Action: Implement practices such as automated testing, code reviews, and canary deployments to catch issues early. Track failure rates and use the data to improve testing and deployment processes.

Integrating DORA Metrics with Other Software Engineering Metrics

While DORA Metrics provide a solid foundation for measuring DevOps performance, they are not exhaustive. Integrating them with other software engineering metrics can provide a more holistic view of engineering performance. Below are use cases and some additional metrics to consider:

Development Cycle Efficiency:

Metrics: Lead Time for Changes and Deployment Frequency

High Deployment Frequency, Swift Lead Time:

Software teams with rapid deployment frequency and short lead time exhibit agile development practices. These efficient processes lead to quick feature releases and bug fixes, ensuring dynamic software development aligned with market demands and ultimately enhancing customer satisfaction.

Low Deployment Frequency despite Swift Lead Time:

A short lead time coupled with infrequent deployments signals potential bottlenecks. Identifying these bottlenecks is vital. Streamlining deployment processes in line with development speed is essential for a software development process.

Code Review Excellence:

Metrics: Comments per PR and Change Failure Rate

Few Comments per PR, Low Change Failure Rate:

Low comments and minimal deployment failures signify high-quality initial code submissions. This scenario highlights exceptional collaboration and communication within the team, resulting in stable deployments and satisfied end-users.

Abundant Comments per PR, Minimal Change Failure Rate:

Teams with numerous comments per PR and a few deployment issues showcase meticulous review processes. Investigating these instances ensures review comments align with deployment stability concerns, ensuring constructive feedback leads to refined code.

Developer Responsiveness:

Metrics: Commits after PR Review and Deployment Frequency

Frequent Commits after PR Review, High Deployment Frequency:

Rapid post-review commits and a high deployment frequency reflect agile responsiveness to feedback. This iterative approach, driven by quick feedback incorporation, yields reliable releases, fostering customer trust and satisfaction.

Sparse Commits after PR Review, High Deployment Frequency:

Despite few post-review commits, high deployment frequency signals comprehensive pre-submission feedback integration. Emphasizing thorough code reviews assures stable deployments, showcasing the team’s commitment to quality.

Quality Deployments:

Metrics: Change Failure Rate and Mean Time to Recovery (MTTR)

Low Change Failure Rate, Swift MTTR:

Low deployment failures and a short recovery time exemplify quality deployments and efficient incident response. Robust testing and a prepared incident response strategy minimize downtime, ensuring high-quality releases and exceptional user experiences.

High Change Failure Rate, Rapid MTTR:

A high failure rate alongside swift recovery signifies a team adept at identifying and rectifying deployment issues promptly. Rapid responses minimize impact, allowing quick recovery and valuable learning from failures, strengthening the team’s resilience.

Impact of PR Size on Deployment:

Metrics: Large PR Size and Deployment Frequency

The size of pull requests (PRs) profoundly influences deployment timelines. Correlating Large PR Size with Deployment Frequency enables teams to gauge the effect of extensive code changes on release cycles.

High Deployment Frequency despite Large PR Size:

Maintaining a high deployment frequency with substantial PRs underscores effective testing and automation. Acknowledge this efficiency while monitoring potential code intricacies, ensuring stability amid complexity.

Low Deployment Frequency with Large PR Size:

Infrequent deployments with large PRs might signal challenges in testing or review processes. Dividing large tasks into manageable portions accelerates deployments, addressing potential bottlenecks effectively.

PR Size and Code Quality:

Metrics: Large PR Size and Change Failure Rate

PR size significantly influences code quality and stability. Analyzing Large PR Size alongside Change Failure Rate allows engineering leaders to assess the link between PR complexity and deployment stability.

High Change Failure Rate with Large PR Size:

Frequent deployment failures with extensive PRs indicate the need for rigorous testing and validation. Encourage breaking down large changes into testable units, bolstering stability and confidence in deployments.

Low Change Failure Rate despite Large PR Size:

A minimal failure rate with substantial PRs signifies robust testing practices. Focus on clear team communication to ensure everyone comprehends the implications of significant code changes, sustaining a stable development environment. Leveraging these correlations empowers engineering teams to make informed, data-driven decisions — a great way to drive business outcomes— optimizing workflows, and boosting overall efficiency. These insights chart a course for continuous improvement, nurturing a culture of collaboration, quality, and agility in software development endeavors.

By combining DORA Metrics with these additional metrics, organizations can gain a comprehensive understanding of their engineering performance and make more informed decisions to drive continuous improvement.

Leveraging Software Engineering Intelligence (SEI) Platforms

As organizations grow, the need for sophisticated tools to manage and analyze engineering metrics becomes apparent. This is where Software Engineering Intelligence (SEI) platforms come into play. SEI platforms like Typo aggregate data from various sources, including version control systems, CI/CD pipelines, project management tools, and incident management systems, to provide a unified view of engineering performance.

Benefits of SEI platforms include:

  • Centralized Metrics Dashboard: A single source of truth for all engineering metrics, providing visibility across teams and projects.
  • Advanced Analytics: Use machine learning and data analytics to identify patterns, predict outcomes, and recommend actions.
  • Customizable Reports: Generate tailored reports for different stakeholders, from engineering teams to executive leadership.
  • Real-time Monitoring: Track key metrics in real-time to quickly identify and address issues.

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By leveraging SEI platforms, large organizations can harness the power of data to drive strategic decision-making and continuous improvement in their engineering practices.

| Implementing DORA Metrics to Improve Dev Performance & Productivity?

Conclusion

In large organizations, aligning engineering work with business goals requires effective communication and the use of standardized metrics. DORA Metrics provides a robust framework for measuring the performance of DevOps and platform engineering, enabling organizations to improve developer efficiency and software delivery processes. By integrating DORA Metrics with other software engineering metrics and leveraging Software Engineering Intelligence platforms, organizations can gain a comprehensive understanding of their engineering performance and drive continuous improvement.

Using DORA Metrics in large organizations not only helps in measuring and enhancing performance but also fosters a culture of data-driven decision-making, ultimately leading to better business outcomes. As the industry continues to evolve, staying abreast of best practices and leveraging advanced tools will be key to maintaining a competitive edge in the software development landscape.

What Lies Ahead: Predictions for DORA Metrics in DevOps

The DevOps Research and Assessment (DORA) metrics have long served as a guiding light for organizations to evaluate and enhance their software development practices.

As we look to the future, what changes lie ahead for DORA metrics amidst evolving DevOps trends? In this blog, we will explore the future landscape and strategize how businesses can stay at the forefront of innovation.

What Are DORA Metrics?

The widely used reference book for engineering leaders called Accelerate introduced the DevOps Research and Assessment (DORA) group’s four metrics, known as the DORA 4 metrics.

These metrics were developed to assist engineering teams in determining two things:

  • The characteristics of a top-performing team.
  • How does their performance compare to the rest of the industry?

Four key DevOps measurements:

Deployment Frequency

Deployment Frequency measures the frequency of deployment of code to production or releases to end-users in a given time frame. Greater deployment frequency is an indication of increased agility and the ability to respond quickly to market demands.

Lead Time for Changes

Lead Time for Changes measures the time between a commit being made and that commit making it to production. Short lead times in software development are crucial for success in today’s business environment. When changes are delivered rapidly, organizations can seize new opportunities, stay ahead of competitors, and generate more revenue.

Change Failure Rate

Change failure rate measures the proportion of deployment to production that results in degraded services. A lower change failure rate enhances user experience and builds trust by reducing failure and helping to allocate resources effectively.

Mean Time to Recover

Mean Time to Recover measures the time taken to recover from a failure, showing the team’s ability to respond to and fix issues. Optimizing MTTR aims to minimize downtime by resolving incidents through production changes and enhancing user satisfaction by reducing downtime and resolution times.

In 2021, DORA introduced Reliability as the fifth metric for assessing software delivery performance.

Reliability

It measures modern operational practices and doesn’t have standard quantifiable targets for performance levels. Reliability comprises several metrics used to assess operational performance including availability, latency, performance, and scalability that measure user-facing behavior, software SLAs, performance targets, and error budgets.

DORA Metrics and Their Role in Measuring DevOps Performance

DORA metrics play a vital role in measuring DevOps performance. It provides quantitative, actionable insights into the effectiveness of an organization’s software delivery and operational capabilities.

  • It offers specific, quantifiable indicators that measure various aspects of software development and delivery process.
  • DORA metrics align DevOps practices with broader business objectives. Metrics like high Deployment Frequency and low Lead Time indicate quick feature delivery and updates to end-users.
  • DORA metrics provide data-driven insights that support informed decision-making at all levels of the organization.
  • It tracks progress over time i.e. enabling teams to measure the effectiveness of implemented changes.
  • DORA metrics help organizations understand and mitigate the risks associated with deploying new code. Aiming to reduce Change Failure Rate and Mean Time to Restore helps software teams increase systems’ reliability and stability.
  • Continuously monitoring DORA metrics helps identify trends and patterns over time, enabling them to pinpoint inefficiencies and bottlenecks in their processes.

This further leads to:

  • Streamlines workflows and fewer failed leads to quick deployments.
  • Reduces failed rate and improved recovery time to minimize downtime and associated risks.
  • Fosters communication and collaboration between the development and operations teams.
  • Faster release and fewer disruptions contribute to a better user experience.

Key Predictions for DORA Metrics in DevOps

Increased Adoption of DORA metrics

One of the major predictions is that the use of DORA metrics in organizations will continue to rise. These metrics will broaden its horizons beyond five key metrics (Deployment Frequency, Lead Time for Changes, Change Failure Rate, Mean Time to Restore, and Reliability) that focus on security, compliance, and more.

Organizations will start integrating these metrics with DevOps tools as well as tracking and reporting on these metrics to benchmark performance against industry leaders. This will allow software development teams to collect, analyze, and act on these data.

Emphasizing Observability and Monitoring

Observability and monitoring are now becoming two non-negotiable aspects of organizations. This is occurring as systems are getting more complex. This makes it challenging for them to understand the system’s state and diagnose issues without comprehensive observability.

Moreover, businesses have started relying on digital services which leads to an increase in the cost of downtime. Metrics like average detection and resolution times can pinpoint and rectify glitches in the early stages. Emphasizing these two aspects will further impact MTTR and CFR by enabling fast detection, and diagnosis of issues.

Integration with SPACE Framework

Nowadays, organizations are seeking more comprehensive and accurate metrics to measure software delivery performance. With the rise in adoption of DORA metrics, they are also said to be integrated well with the SPACE framework.

Since DORA and SPACE are both complemented in nature, integrating will provide a more holistic view. While DORA focuses on technical outcome and efficiency, the SPACE framework provides a broader perspective that incorporates aspects of developer satisfaction, collaboration, and efficiency (all about human factors). Together, they both emphasize the importance of continuous improvement and faster feedback loops.

Merging with AI and ML Advancements

AI and ML technologies are emerging. By integrating these tools with DORA metrics, development teams can leverage predictive analytics, proactively identify potential issues, and promote AI-driven decision-making.

DevOps gathers extensive data from diverse sources, which AI and ML tools can process and analyze more efficiently than manual methods. These tools enable software teams to automate decisions based on DORA metrics. For instance, if a deployment is forecasted to have a high failure rate, the tool can automatically initiate additional testing or notify the relevant team member.

Furthermore, continuous analysis of DORA metrics allows teams to pinpoint areas for improvement in the development and deployment processes. They can also create dashboards that highlight key metrics and trends.

Emphasis on Cultural Transformation

DORA metrics alone are insufficient. Engineering teams need more than tools and processes. Soon, there will be a cultural transformation emphasizing teamwork, open communication, and collective accountability for results. Factors such as team morale, collaboration across departments, and psychological safety will be as crucial as operational metrics.

Collectively, these elements will facilitate data-driven decision-making, adaptability to change, experimentation with new concepts, and fostering continuous improvement.

Focus on Security Metrics

As cyber-attacks continue to increase, security is becoming a critical concern for organizations. Hence, a significant upcoming trend is the integration of security with DORA metrics. This means not only implementing but also continually measuring and improving these security practices. Such integration aims to provide a comprehensive view of software development performance. This also allows striking a balance between speed and efficiency on one hand, and security and risk management on the other.

How to Stay Ahead of the Curve?

Stay Informed

Ensure monitoring of industry trends, research, and case studies continuously related to DORA metrics and DevOps practices.

Experiment and Implement

Don’t hesitate to pilot new DORA metrics and DevOps techniques within your organization to see what works best for your specific context.

Embrace Automation

Automate as much as possible in your software development and delivery pipeline to improve speed, reliability, and the ability to collect metrics effectively.

Collaborate across Teams

Foster collaboration between development, operations, and security teams to ensure alignment on DORA metrics goals and strategies.

Continuous Improvement

Regularly review and optimize your DORA metrics implementation based on feedback and new insights gained from data analysis.

Cultural Alignment

Promote a culture that values continuous improvement, learning, and transparency around DORA metrics to drive organizational alignment and success.

How Typo Leverages DORA Metrics?

Typo is an effective software engineering intelligence platform that offers SDLC visibility, developer insights, and workflow automation to build better programs faster. It offers comprehensive insights into the deployment process through key DORA metrics such as change failure rate, time to build, and deployment frequency.

DORA Metrics Dashboard

Typo’s DORA metrics dashboard has a user-friendly interface and robust features tailored for DevOps excellence. The dashboard pulls in data from all the sources and presents it in a visualized and detailed way to engineering leaders and the development team.

Comprehensive Visualization of Key Metrics

Typo’s dashboard provides clear and intuitive visualizations of the four key DORA metrics: Deployment Frequency, Change Failure Rate, Lead Time for Changes, and Mean Time to Restore.

Benchmarking for Context

By providing benchmarks, Typo allows teams to compare their performance against industry standards, helping them understand where they stand. It also allows the team to compare their current performance with their historical data to track improvements or identify regressions.

Find out what it takes to build reliable high-velocity dev teams

Conclusion

The rising adoption of DORA metrics in DevOps marks a significant shift towards data-driven software delivery practices. Integrating these metrics with operations, tools, and cultural frameworks enhances agility and resilience. It is crucial to stay ahead of the curve by keeping an eye on trends, embracing automation, and promoting continuous improvement to effectively harness DORA metrics to drive innovation and achieve sustained success.

How to Calculate Cycle Time

Cycle time is one of the important metrics in software development. It measures the time taken from the start to the completion of a process, providing insights into the efficiency and productivity of teams. Understanding and optimizing cycle time can significantly improve overall performance and customer satisfaction.

This blog will guide you through the precise cycle time calculation, highlighting its importance and providing practical steps to measure and optimize it effectively.

What is Cycle Time?

Cycle time measures the total elapsed time taken to complete a specific task or work item from the beginning to the end of the process.

  • The “Coding” stage represents the time taken by developers to write and complete the code changes.
  • The “Pickup” stage denotes the time spent before a pull request is assigned for review.
  • The “Review” stage encompasses the time taken for peer review and feedback on the pull request.
  • Finally, the “Merge” stage shows the duration from the approval of the pull request to its integration into the main codebase.

Screenshot 2024-03-16 at 1.14.10 AM.png

It is important to differentiate cycle time from other related metrics such as lead time, which includes all delays and waiting periods, and takt time, which is the rate at which a product needs to be completed to meet customer demand. Understanding these differences is crucial for accurately measuring and optimizing cycle time.

Components of Cycle Time Calculation

To calculate total cycle time, you need to consider several components:

  • Net production time: The total time available for production, excluding breaks, maintenance, and downtime.
  • Work items and task duration: Specific tasks or work items and the time taken to complete each.
  • Historical data: Past data on task durations and production times to ensure accurate calculations.

Step-by-Step Guide to Calculating Cycle Time

Step 1: Identify the start and end points of the process:

Clearly define the beginning and end of the process you are measuring. This could be initiating and completing a task in a project management tool.

Step 2: Gather the necessary data

Collect data on task durations and time tracking. Use tools like time-tracking software to ensure accurate data collection.

Step 3: Calculate net production time

Net production time is the total time available for production minus any non-productive time. For example, if a team works 8 hours daily but takes 1 hour for breaks and meetings, the net production time is 7 hours.

Step 4: Apply the cycle time formula

The formula for cycle time is:

Cycle Time = Net Production Time / Number of Work Items Completed

Cycle Time= Number of Work Items Completed / Net Production Time

Example calculation

If a team has a net production time of 35 hours in a week and completes 10 tasks, the cycle time is:

Cycle Time = 35 hours / 10 tasks = 3.5 hours per task

Cycle Time= 10 tasks / 35 hours =3.5 hours per task

An ideal cycle time should be less than 48 hours. Shorter cycle times in software development indicate that teams can quickly respond to requirements, deliver features faster, and adapt to changes efficiently, reflecting agile and responsive development practices.

Longer cycle times in software development typically indicates several potential issues or conditions within the development process. This can lead to increased costs and delayed delivery of features.

Accounting for Variations in Work Item Complexity

When calculating cycle time, it is crucial to account for variations in the complexity and size of different work items. Larger or more complex tasks can skew the average cycle time. To address this, categorize tasks by size or complexity and calculate cycle time for each category separately.

Use of Control Charts

Control charts are a valuable tool for visualizing cycle time data and identifying trends or anomalies. You can quickly spot variations and investigate their causes by plotting cycle times on a control chart.

Statistical Analysis

Performing statistical analysis on cycle time data can provide deeper insights into process performance. Metrics such as standard deviation and percentiles help understand the distribution and variability of cycle times, enabling more precise optimization efforts.

Tools and Techniques for Accurate Measurement

In order to effectively track task durations and completion times, it’s important to utilize time tracking tools and software such as Jira, Trello, or Asana. These tools can provide a systematic approach to managing tasks and projects by allowing team members to log their time and track task durations consistently.

Consistent data collection is essential for accurate time tracking. Encouraging all team members to consistently log their time and task durations ensures that the data collected is reliable and can be used for analysis and decision-making.

Visual management techniques, such as implementing Kanban boards or other visual tools, can be valuable for tracking progress and identifying bottlenecks in the workflow. These visual aids provide a clear and transparent view of task status and can help teams address any delays or issues promptly.

Optimizing cycle time involves analyzing cycle time data to identify bottlenecks in the workflow. By pinpointing areas where tasks are delayed, teams can take action to remove these bottlenecks and optimize their processes for improved efficiency.

Continuous improvement practices, such as implementing Agile and Lean methodologies, are effective for improving cycle times continuously. These practices emphasize a flexible and iterative approach to project management, allowing teams to adapt to changes and make continuous improvements to their processes.

Furthermore, studying case studies of successful cycle time reduction from industry leaders can provide valuable insights into efficient practices that have led to significant reductions in cycle times. Learning from these examples can inspire and guide teams in implementing effective strategies to reduce cycle times in their own projects and workflows.

How Typo Helps?

Typo is an innovative tool designed to enhance the precision of cycle time calculations and overall productivity.

It seamlessly integrates Git data by analyzing timestamps from commits and merges. This integration ensures that cycle time calculations are based on actual development activities, providing a robust and accurate measurement compared to relying solely on task management tools. This empowers teams with actionable insights for optimizing their workflow and enhancing productivity in software development projects.

Here’s how Typo can help:

Automated time tracking: Typo provides automated time tracking for tasks, eliminating manual entry errors and ensuring accurate data collection.

Real-time analytics: With Typo, you can access real-time analytics to monitor cycle times, identify trends, and make data-driven decisions.

Customizable dashboards: Typo offers customizable dashboards that allow you to visualize cycle time data in a way that suits your needs, making it easier to spot inefficiencies and areas for improvement.

Seamless integration: Typo integrates seamlessly with popular project management tools, ensuring that all your data is synchronized and up-to-date.

Continuous improvement support: Typo supports continuous improvement by providing insights and recommendations based on your cycle time data, helping you implement best practices and optimize your workflows.

By leveraging Typo, you can achieve more precise cycle time calculations, improving efficiency and productivity.

Common Challenges and Solutions

In dealing with variability in task durations, it’s important to use averages as well as historical data to account for the range of possible durations. By doing this, you can better anticipate and plan for potential fluctuations in timing.

When it comes to ensuring data accuracy, it’s essential to implement a system for regularly reviewing and validating data. This can involve cross-referencing data from different sources and conducting periodic audits to verify its accuracy.

Additionally, when balancing speed and quality, the focus should be on maintaining high-quality standards while optimizing cycle time to ensure customer satisfaction. This can involve continuous improvement efforts aimed at increasing efficiency without compromising the quality of the final output.

The Path Forward with Optimized Cycle Time

Accurately calculating and optimizing cycle time is essential for improving efficiency and productivity. By following the steps outlined in this blog and utilizing tools like Typo, you can gain valuable insights into your processes and make informed decisions to enhance performance. Start measuring your cycle time today and reap the benefits of precise and optimized workflows.

DevOps Metrics Mistakes to Avoid in 2024

As DevOps practices continue to evolve, it’s crucial for organizations to effectively measure DevOps metrics to optimize performance.

Here are a few common mistakes to avoid when measuring these metrics to ensure continuous improvement and successful outcomes:

DevOps Landscape in 2024

In 2024, the landscape of DevOps metrics continues to evolve, reflecting the growing maturity and sophistication of DevOps practices. The emphasis is to provide actionable insights into the development and operational aspects of software delivery.

The integration of AI and machine learning (ML) in DevOps has become increasingly significant in transforming how teams monitor, manage, and improve their software development and operations processes. Apart from this, observability and real-time monitoring have become critical components of modern DevOps practices in 2024. They provide deep insights into system behavior and performance and are enhanced significantly by AI and ML technologies.

Lastly, Organizations are prioritizing comprehensive, real-time, and predictive security metrics to enhance their security posture and ensure robust incident response mechanisms.

Importance of Measuring DevOps Metrics

DevOps metrics track both technical capabilities and team processes. They reveal the performance of a DevOps software development pipeline and help to identify and remove any bottlenecks in the process in the early stages.

Below are a few benefits of measuring DevOps metrics:

  • Metrics enable teams to identify bottlenecks, inefficiencies, and areas for improvement. By continuously monitoring these metrics, teams can implement iterative changes and track their effectiveness.
  • DevOps metrics help in breaking down silos between development, operations, and other teams by providing a common language and set of goals. It improves transparency and visibility into the workflow and fosters better collaboration and communication.
  • Metrics ensure the team’s efforts are aligned with customer needs and expectations. Faster and more reliable releases contribute to better customer experiences and satisfaction.
  • DevOps metrics provide objective data that can be used to make informed decisions rather than relying on intuition or subjective opinions. This data-driven approach helps prioritize tasks and allocate resources effectively.
  • DevOps Metrics allows teams to set benchmarks and track progress against them. Clear goals and measurable targets motivate teams and provide a sense of achievement when milestones are reached.

Common Mistakes to Avoid when Measuring DevOps Metrics

Not Defining Clear Objectives

When clear objectives are not defined for development teams, they may measure metrics that do not directly contribute to strategic goals. This leads to scattered efforts and teams may achieve high numbers in certain metrics without realizing they are not contributing meaningfully to overall business objectives. This may also not provide actionable insights and decisions might be based on incomplete or misleading data. Lack of clear objectives makes it challenging to evaluate performance accurately and makes it unclear whether performance is meeting expectations or falling short.

Solutions

Below are a few ways to define clear objectives for DevOps metrics:

  • Start by understanding the high-level business goals. Engage with stakeholders to identify what success looks like for the organization.
  • Based on the business goals, identify specific KPIs that can measure progress towards these goals.
  • Ensure that objectives are Specific, Measurable, Achievable, Relevant, and Time-bound (SMART). For example, “Reduce the average lead time for changes from 5 days to 3 days within the next quarter.”
  • Choose metrics that directly measure progress toward the objectives.
  • Regularly review the objectives and the metrics to ensure they remain aligned with evolving business goals and market conditions. Adjust them as needed to reflect new priorities or insights.

Prioritizing Speed over Quality

Organizations usually focus on delivering products quickly rather than quality. However, speed and quality must work hand in hand. DevOps tasks must be accomplished by maintaining high standards and must be delivered to the end users on time. Due to this, the development team often faces intense pressure to deliver products or updates rapidly to stay competitive in the market. This can lead them to focus excessively on speed metrics, such as deployment frequency or lead time for changes, at the expense of quality metrics.

Solutions

  • Clearly define quality goals alongside speed goals. This involves setting targets for reliability, performance, security, and user experience metrics that are equally important as delivery speed metrics.
  • Implement continuous feedback loops throughout the DevOps process such as feedback from users, automated testing, monitoring, and post-release reviews.
  • Invest in automation and tooling that accelerates delivery as well as enhances quality. Automated testing, continuous integration, and continuous deployment (CI/CD) pipelines can help in achieving both speed and quality goals simultaneously.
  • Educate teams about the importance of balancing speed and quality in DevOps practices.
  • Regularly review and refine metrics based on the evolving needs of the organization and the feedback received from customers and stakeholders.

Tracking Too Much at Once

It is usually believed that the more metrics you track, the better you’ll understand DevOps processes. This leads to an overwhelming number of metrics, where most of them are redundant or not directly actionable. It usually occurs when there is no clear strategy or prioritization framework, leading teams to attempt to measure everything that further becomes difficult to manage and interpret. Moreover, it also results in tracking numerous metrics to show detailed performance, even if those metrics are not particularly meaningful.

Solutions

  • Identify and focus on a few key metrics that are most relevant to your business goals and DevOps objectives.
  • Align your metrics with clear objectives to ensure you are tracking the most impactful data. For example, if your goal is to improve deployment frequency and reliability, focus on metrics like deployment frequency, lead time for changes, and mean time to recovery.
  • Review the metrics you are tracking to determine their relevance and effectiveness. Remove metrics that do not provide value or are redundant.
  • Foster a culture that values the quality and relevance of metrics over the sheer quantity.
  • Use visualizations and summaries to highlight the most important data, making it easier for stakeholders to grasp the critical information without being overwhelmed by the volume of metrics.

Rewarding Performance

Engineering leaders often believe that rewarding performance will motivate developers to work harder and achieve better results. However, this is not true. Rewarding specific metrics can lead to an overemphasis on those metrics at the expense of other important aspects of work. For example, focusing solely on deployment frequency might lead to neglecting code quality or thorough testing. This can also result in short-term improvements but leads to long-term problems such as burnout, reduced intrinsic motivation, and a decline in overall quality. Due to this, developers may manipulate metrics or take shortcuts to achieve rewarded outcomes, compromising the integrity of the process and the quality of the product.

Solutions

  • Cultivate an environment where teams are motivated by the satisfaction of doing good work rather than external rewards.
  • Recognize and appreciate good work through non-monetary means such as public acknowledgment, opportunities for professional development, and increased autonomy.
  • Instead of rewarding individual performance, measure and reward team performance.
  • Encourage knowledge sharing, pair programming, and cross-functional teams to build a cooperative work environment.
  • If rewards are necessary, align them with long-term goals rather than short-term performance metrics.

Lack of Continuous Integration and Testing

Without continuous integration and testing, bugs and defects are more likely to go undetected until later stages of development or production, leading to higher costs and more effort to fix issues. It compromises the quality of the software, resulting in unreliable and unstable products that can damage the organization’s reputation. Moreover, it can result in slower progress over time due to the increased effort required to address accumulated technical debt and defects.

Solutions

  • Allocate resources to implement CI/CD pipelines and automated testing frameworks.
  • Invest in training and upskilling team members on CI/CD practices and tools.
  • Begin with small, incremental implementations of CI and testing. Gradually expand the scope as the team becomes more comfortable and proficient with the tools and processes.
  • Foster a culture that values quality and continuous improvement. Encourage collaboration between development and operations teams to ensure that CI and testing are seen as essential components of the development process.
  • Use automation to handle repetitive and time-consuming tasks such as building, testing, and deploying code. This reduces manual effort and increases efficiency.

Key DevOps Metrics to Measure

Below are a few important DevOps metrics:

Deployment Frequency

Deployment Frequency measures the frequency of code deployment to production and reflects an organization’s efficiency, reliability, and software delivery quality. It is often used to track the rate of change in software development and highlight potential areas for improvement.

Lead Time for Changes

Lead Time for Changes is a critical metric used to measure the efficiency and speed of software delivery. It is the duration between a code change being committed and its successful deployment to end-users. This metric is a good indicator of the team’s capacity, code complexity, and efficiency of the software development process.

Change Failure Rate

Change Failure Rate measures the frequency at which newly deployed changes lead to failures, glitches, or unexpected outcomes in the IT environment. It reflects the stability and reliability of the entire software development and deployment lifecycle. It is related to team capacity, code complexity, and process efficiency, impacting speed and quality.

Mean Time to Recover

Mean Time to Recover is a valuable metric that calculates the average duration taken by a system or application to recover from a failure or incident. It is an essential component of the DORA metrics and concentrates on determining the efficiency and effectiveness of an organization’s incident response and resolution procedures.

Conclusion

Optimizing DevOps practices requires avoiding common mistakes in measuring metrics. To optimize DevOps practices and enhance organizational performance, specialized tools like Typo can help simplify the measurement process. It offers customized DORA metrics and other engineering metrics that can be configured in a single dashboard.

Top Platform Engineering Tools (2024)

Platform engineering tools empower developers by enhancing their overall experience. By eliminating bottlenecks and reducing daily friction, these tools enable developers to accomplish tasks more efficiently. This efficiency translates into improved cycle times and higher productivity.

In this blog, we explore top platform engineering tools, highlighting their strengths and demonstrating how they benefit engineering teams.

What is Platform Engineering?

Platform Engineering, an emerging technology approach, enables the software engineering team with all the required resources. This is to help them perform end-to-end operations of software development lifecycle automation. The goal is to reduce overall cognitive load, enhance operational efficiency, and remove process bottlenecks by providing a reliable and scalable platform for building, deploying, and managing applications.

Importance of Platform Engineering

  • Platform engineering involves creating reusable components and standardized processes. It also automates routine tasks, such as deployment, monitoring, and scaling, to speed up the development cycle.
  • Platform engineers integrate security measures into the platform, to ensure that applications are built and deployed securely. They help ensure that the platform meets regulatory and compliance requirements.
  • It ensures efficient use of resources to balance performance and expenditure. It also provides transparency into resource usage and associated costs to help organizations make informed decisions about scaling and investment.
  • By providing tools, frameworks, and services, platform engineers empower developers to build, deploy, and manage applications more effectively.
  • A well-engineered platform allows organizations to adapt quickly to market changes, new technologies, and customer needs.

Best Platform Engineering Tools

Typo

Typo is an effective software engineering intelligence platform that offers SDLC visibility, developer insights, and workflow automation to build better programs faster. It can seamlessly integrate into tech tool stacks such as GIT versioning, issue tracker, and CI/CD tools.

It also offers comprehensive insights into the deployment process through key metrics such as change failure rate, time to build, and deployment frequency. Moreover, its automated code tool helps identify issues in the code and auto-fixes them before you merge to master.

Typo has an effective sprint analysis feature that tracks and analyzes the team’s progress throughout a sprint. Besides this, It also provides 360 views of the developer experience i.e. captures qualitative insights and provides an in-depth view of the real issues.

Kubernetes

An open-source container orchestration platform. It is used to automate deployment, scale, and manage container applications.

Kubernetes is beneficial for application packages with many containers; developers can isolate and pack container clusters to be deployed on several machines simultaneously.

Through Kubernetes, engineering leaders can create Docker containers automatically and assign them based on demands and scaling needs.
Kubernetes can also handle tasks like load balancing, scaling, and service discovery for efficient resource utilization. It also simplifies infrastructure management and allows customized CI/CD pipelines to match developers’ needs.

Jenkins

An open-source automation server and CI/CD tool. Jenkins is a self-contained Java-based program that can run out of the box.

It offers extensive plug-in systems to support building and deploying projects. It supports distributing build jobs across multiple machines which helps in handling large-scale projects efficiently. Jenkins can be seamlessly integrated with various version control systems like Git, Mercurial, and CVS and communication tools such as Slack, and JIRA.

GitHub Actions

A powerful platform engineering tool that automates software development workflows directly from GitHub.GitHub Actions can handle routine development tasks such as code compilation, testing, and packaging for standardizedizing and efficient processes.

It creates custom workflows to automate various tasks and manage blue-green deployments for smooth and controlled application deployments.

GitHub Actions allows engineering teams to easily deploy to any cloud, create tickets in Jira, or publish packages.

GitLab CI

GitLab CI automatically uses Auto DevOps to build, test, deploy, and monitor applications. It uses Docker images to define environments for running CI/CD jobs and build and publish them within pipelines. It supports parallel job execution that allows to running of multiple tasks concurrently to speed up build and test processes.

GitLab CI provides caching and artifact management capabilities to optimize build times and preserve build outputs for downstream processes. It can be integrated with various third-party applications including CircleCI, Codefresh, and YouTrack.

AWS Codepipeline

A Continuous Delivery platform provided by Amazon Web Services (AWS). AWS Codepipeline automates the release pipeline and accelerates the workflow with parallel execution.

It offers high-level visibility and control over the build, test, and deploy processes. It can be integrated with other AWS tools such as AWS Codebuild, AWS CodeDeploy, and AWS Lambda as well as third-party integrations like GitHub, Jenkins, and BitBucket.

AWS Codepipeline can also configure notifications for pipeline events to help stay informed about the deployment state.

Argo CD

A Github-based continuous deployment tool for Kubernetes application. Argo CD allows to deployment of code changes directly to Kubernetes resources.

It simplifies the management of complex application deployment and promotes a self-service approach for developers. Argo CD defines and automates the K8 cluster to suit team needs and includes multi-cluster setups for managing multiple environments.

It can seamlessly integrate with third-party tools such as Jenkins, GitHub, and Slack. Moreover, it supports multiple templates for creating Kubernetes manifests such as YAML files and Helm charts.

Azure DevOps Pipeline

A CI/CD tool offered by Microsoft Azure. It supports building, testing, and deploying applications using CI/CD pipelines within the Azure DevOps ecosystem.

Azure DevOps Pipeline lets engineering teams define complex workflows that handle tasks like compiling code, running tests, building Docker images, and deploying to various environments. It can automate the software delivery process, reducing manual intervention, and seamlessly integrates with other Azure services, such as Azure Repos, Azure Artifacts, and Azure Kubernetes Service (AKS).

Moreover, it empowers DevSecOps teams with a self-service portal for accessing tools and workflows.

Terraform

An Infrastructure as Code (IoC) tool. It is a well-known cloud-native platform in the software industry that supports multiple cloud provider and infrastructure technologies.

Terraform can quickly and efficiently manage complex infrastructure and can centralize all the infrastructures. It can seamlessly integrate with tools like Oracle Cloud, AWS, OpenStack, Google Cloud, and many more.

It can speed up the core processes the developers’ team needs to follow. Moreover, Terraform automates security based on the enforced policy as the code.

Heroku

A platform-as-a-service (PaaS) based on a managed container system. Heroku enables developers to build, run, and operate applications entirely in the cloud and automates the setup of development, staging, and production environments by configuring infrastructure, databases, and applications consistently.

It supports multiple deployment methods, including Git, GitHub integration, Docker, and Heroku CLI, and includes built-in monitoring and logging features to track application performance and diagnose issues.

Circle CI

A popular Continuous Integration/Continuous Delivery (CI/CD) tool that allows software engineering teams to build, test, and deploy software using intelligent automation. It hosts CI under the cloud-managed option.

Circle CI is GitHub-friendly and includes extensive API for customized integrations. It supports parallelism i.e. splitting tests across different containers to run as clean and separate builds. It can also be configured to run complex pipelines.

Circle CI has an in-built feature ‘Caching’. It speeds up builds by storing dependencies and other frequently-used files, reducing the need to re-download or recompile them for subsequent builds.

How to Choose the Right Platform Engineering Tools?

Know your Requirements

Understand what specific problems or challenges the tools need to solve. This could include scalability, automation, security, compliance, etc. Consider inputs from stakeholders and other relevant teams to understand their requirements and pain points.

Evaluate Core Functionalities

List out the essential features and capabilities needed in platform engineering tools. Also, the tools must integrate well with existing infrastructure, development methodologies (like Agile or DevOps), and technology stack.

Security and Compliance

Check if the tools have built-in security features or support integration with security tools for vulnerability scanning, access control, encryption, etc. The tools must comply with relevant industry regulations and standards applicable to your organization.

Documentation and Support

Check the availability and quality of documentation, tutorials, and support resources. Good support can significantly reduce downtime and troubleshooting efforts.

Flexibility

Choose tools that are flexible and adaptable to future technology trends and changes in the organization’s needs. The tools must integrate smoothly with the existing toolchain, including development frameworks, version control systems, databases, and cloud services.

Proof of Concept (PoC)

Conduct a pilot or proof of concept to test how well the tools perform in the environment. This allows them to validate their suitability before committing to full deployment.

Conclusion

Platform engineering tools play a crucial role in the IT industry by enhancing the experience of software developers. They streamline workflows, remove bottlenecks, and reduce friction within developer teams, thereby enabling more efficient task completion and fostering innovation across the software development lifecycle.

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Mastering the Art of DORA Metrics

In today's competitive tech landscape, engineering teams need robust and actionable metrics to measure and improve their performance. The DORA (DevOps Research and Assessment) metrics have emerged as a standard for assessing software delivery performance. In this blog, we'll explore what DORA metrics are, why they're important, and how to master their implementation to drive business success.

What are DORA Metrics?

DORA metrics, developed by the DORA team, are key performance indicators that measure the performance of DevOps and engineering teams. They are the standard framework to track the effectiveness and efficiency of software development and delivery processes. Optimizing DORA Metrics helps achieve optimal speed, quality, and stability and provides a data-driven approach to evaluating the operational practices' impact on software delivery performance.

The four key DORA metrics are:

  • Deployment Frequency measures how often an organization deploys code to production per week. One deployment per week is standard. However, it also depends on the type of product.
  • Lead Time for Changes tracks the time it takes for a commit to go into production. The standard for Lead time for Change is less than one day for elite performers and between one day and one week for high performers.
  • Change Failure Rate measures the percentage of deployments causing a failure in production. 0% - 15% CFR is considered to be a good indicator of code quality.
  • Mean Time to Restore (MTTR) indicates the time it takes to recover from a production failure. Less than one hour is considered to be a standard for teams.

In 2021, the DORA Team added Reliability as a fifth metric. It is based upon how well the user’s expectations are met, such as availability and performance, and measures modern operational practices.

But, Why are they Important?

These metrics offer a comprehensive view of the software delivery process, highlighting areas for improvement and enabling software teams to enhance their delivery speed, reliability, and overall quality, leading to better business outcomes.

Objective Measurement of Performance

DORA metrics provide an objective way to measure the performance of software delivery processes. By focusing on these key indicators, dev teams gain a clear and quantifiable understanding of their tech practices.

Benchmarking Against Industry Standards

DORA metrics enable organizations to benchmark their performance against industry standards. The DORA State of DevOps reports provide insights into what high-performing teams look like, offering a target for other organizations to aim for. By comparing your metrics against these benchmarks, you can set realistic goals and understand where your team stands to others in the industry.

Enhancing Collaboration and Communication

DORA metrics promote better collaboration and communication within and across teams. By providing a common language and set of goals, these metrics align development, operations, and business teams around shared objectives. This alignment helps in breaking down silos and fostering a culture of collaboration and transparency.

Improving Business Outcomes

The ultimate goal of tracking DORA metrics is to improve business outcomes. High-performing teams, as measured by DORA metrics, are correlated with faster delivery times, higher quality software, and improved stability. These improvements lead to greater customer satisfaction, increased market competitiveness, and higher revenue growth.

Identify Trends and Issues

Analyzing DORA metrics helps DevOps teams identify performance trends and pinpoint bottlenecks in their software delivery lifecycle (SDLC). This allows them to address issues proactively, and improve developer experiences and overall workflow efficiency.

Value Stream Management

Integrating DORA metrics into value stream management practices enables organizations to optimize their software delivery processes. Analyzing DORA metrics allows teams to identify inefficiencies and bottlenecks in their value streams and inform teams where to focus their improvement efforts in the context of VSM.

So, How do we Master the Implementation?

Define Clear Objectives

Firstly, engineering leaders must identify what they want to achieve by tracking DORA metrics. Objectives might include increasing deployment frequency, reducing lead time, decreasing change failure rates, or minimizing MTTR.

Collect Accurate Data

Ensure your tools are properly configured to collect the necessary data for each metric:

  • Deployment Frequency: Track every deployment to production.
  • Lead Time for Changes: Measure the time from code commit to deployment.
  • Change Failure Rate: Monitor production incidents and link them to specific changes.
  • MTTR: Track the time taken from the detection of a failure to resolution.

Analyze and Visualize Data

Use dashboards and reports to visualize the metrics. There are many DORA metrics trackers available in the market. Do research and select a tool that can help you create clear and actionable visualizations.

Set Benchmarks and Targets

Establish benchmarks based on industry standards or your historical data. Set realistic targets for improvement and use these as a guide for your DevOps practices.

Encourage Continuous Improvement

Use the insights gained from your DORA metrics to identify bottlenecks and areas for improvement. Ensure to implement changes and continuously monitor their impact on your metrics. This iterative approach helps in gradually enhancing your DevOps performance.

Educate teams and foster a data-driven culture

Train software development teams on DORA metrics and promote a culture that values data-driven decision-making and learning from metrics. Also, encourage teams to discuss DORA metrics in retrospectives and planning meetings.

Regular Reviews and Adjustments

Regularly review metrics and adjust your practices as needed. The objectives and targets must evolve with the organization’s growth and changes in the industry. Typo is an intelligent engineering management platform for gaining visibility, removing blockers, and maximizing developer effectiveness. Its user-friendly interface and cutting-edge capabilities set it apart in the competitive landscape.

Key Features

  • Customizable DORA metrics dashboard: You can tailor the DORA metrics dashboard to their specific needs, providing a personalized and efficient monitoring experience. It provides a user-friendly interface and integrates with DevOps tools to ensure a smooth data flow for accurate metric representation.
  • Code review automation: Typo is an automated code review tool that not only enables developers to catch issues related to code maintainability, readability, and potential bugs but also can detect code smells. It identifies issues in the code and auto-fixes them before you merge to master.
  • Predictive sprint analysis: Typo’s intelligent algorithm provides you with complete visibility of your software delivery performance and proactively tells which sprint tasks are blocked, or are at risk of delay by analyzing all activities associated with the task.
  • Measures developer experience: While DORA metrics provide valuable insights, they alone cannot fully address software delivery and team performance. With Typo’s research-backed framework, gain qualitative insights across developer productivity and experience to know what’s causing friction and how to improve.
  • High number of integrations: Typo seamlessly integrates with the tech tool stack. It includes GIT versioning, Issue tracker, CI/CD, communication, Incident management, and observability tools.

‍Conclusion

Understanding DORA metrics and effectively implementing and analyzing them can significantly enhance your software delivery performance and overall DevOps practices. These key metrics are vital for benchmarking against industry standards, enhancing collaboration and communication, and improving business outcomes.

Gartner’s Report on Software Engineering Intelligence Platforms 2024

Introduction

As a leading vendor in the software engineering intelligence (SEI) platform space, we at Typo, are pleased to present this summary report. This document synthesizes key findings from Gartner’s comprehensive analysis and incorporates our own insights to help you better understand the evolving landscape of SEI platforms. Our aim is to provide clarity on the benefits, challenges, and future directions of these platforms, highlighting their potential to revolutionize software engineering productivity and value delivery.

Overview

The Software Engineering Intelligence (SEI) platform market is rapidly growing, driven by the increasing need for software engineering leaders to use data to demonstrate their teams’ value. According to Gartner, this nascent market offers significant potential despite its current size. However, leaders face challenges such as fragmented data across multiple systems and concerns over adding new tools that may be perceived as micromanagement by their teams.

Key Findings

1. Market Growth and Challenges

  • The SEI platform market is expanding but remains in its early stages.
  • With many vendors offering similar capabilities, software engineering leaders find it challenging to navigate this evolving market.
  • There is pressure to use data to showcase team value, but data is often scattered across various systems, complicating its collection and analysis.
  • Leaders are cautious about introducing new tools into an already crowded landscape, fearing it could be seen as micromanagement, potentially eroding trust.

2. Value of SEI Platforms

  • SEI platforms can significantly enhance the collection and analysis of software engineering data, helping track key indicators of product success like value creation and developer productivity. According to McKinsey & Company, high-performing organizations utilize data-driven insights to boost developer productivity and achieve superior business outcomes.
  • These platforms offer a comprehensive view of engineering processes, enabling continuous improvement and better business alignment.

3. Market Adoption Projections

  • SEI platform adoption is projected to rise significantly, from 5% in 2024 to 50% by 2027, as organizations seek to leverage data for increased productivity and value delivery.
SEI platform adoption

4. Platform Capabilities

  • SEI platforms provide data-driven visibility into engineering teams’ use of time and resources, operational effectiveness, and progress on deliverables. They integrate data from common engineering tools and systems, offering tailored, role-specific user experiences.
  • Key capabilities include data collection, analysis, reporting, and dashboard creation. Advanced features such as AI/ML-driven insights and conversational interfaces are becoming increasingly prevalent, helping reduce cognitive load and manual tasks.

Recommendations

Proof of Concept (POC)

  • Engage in POC processes to verify that SEI platforms can drive measurable improvements.
  • This step ensures the chosen platform can provide actionable insights that lead to better outcomes.

Improve Data Collection and Analysis

  • Utilize SEI platforms to track essential metrics and demonstrate the value delivered by engineering teams.
  • Effective data collection and analysis are crucial for visibility into software engineering trends and for boosting productivity.

Avoid Micromanagement Perceptions

  • Involve both teams and managers in the evaluation process to ensure the platform meets everyone’s needs, mitigating fears of micromanagement.
  • Gartner emphasizes the importance of considering the needs of both practitioners and leaders to ensure broad acceptance and utility.

Strategic Planning Assumption

By 2027, the use of SEI platforms by software engineering organizations to increase developer productivity is expected to rise to 50%, up from 5% in 2024, driven by the necessity to deliver quantifiable value through data-driven insights.

Wanna Stay Ahead & Deliver Quantifiable Value?

Market Definition

Gartner defines SEI platforms as solutions that provide software engineering leaders with data-driven visibility into their teams’ use of time and resources, operational effectiveness, and progress on deliverables. These platforms must ingest and analyze signals from common engineering tools, offering tailored user experiences for easy data querying and trend identification.

Market Direction and Trends

Increasing Interest

There is growing interest in SEI platforms and engineering metrics. Gartner notes that client interactions on these topics doubled from 2022 to 2023, reflecting a surge in demand for data-driven insights in software engineering.

Competitive Dynamics

Existing DevOps and agile planning tools are evolving to include SEI-type features, creating competitive pressure and potential market consolidation. Vendors are integrating more sophisticated dashboards, reporting, and insights, impacting the survivability of standalone SEI platform vendors.

AI-Powered Features

SEI platforms are increasingly incorporating AI to reduce cognitive load, automate tasks, and provide actionable insights. According to Forrester, AI-driven insights can significantly enhance software quality and team efficiency by enabling proactive management strategies.

Adoption Drivers

Visibility into Engineering Data

Crucial for boosting developer productivity and achieving business outcomes. High-performing organizations leverage tools that track and report engineering metrics to enhance productivity.

Tooling Rationalization

SEI platforms can potentially replace multiple existing tools, serving as the main dashboard for engineering leadership. This consolidation simplifies the tooling landscape and enhances efficiency.

Efficiency Focus

With increased operating budgets, there is a strong focus on tools that drive efficient and effective execution, helping engineering teams improve delivery and meet performance objectives.

Market Analysis

SEI platforms address several common use cases:

Reporting and Benchmarking

Provide data-driven answers to questions about team activities and performance. Collecting and conditioning data from various engineering tools enables effective dashboards and reports, facilitating benchmarking against industry standards.

Insight Discovery

Generate insights through multivariate analysis of normalized data, such as correlations between quality and velocity. These insights help leaders make informed decisions to drive better outcomes.

Recommendations

Deliver actionable insights backed by recommendations. Tools may suggest policy changes or organizational structures to improve metrics like lead times. According to DORA, organizations leveraging key metrics like Deployment Frequency and Lead Time for Changes tend to have higher software delivery performance.

Improving Developer Productivity with Tools and Metrics

SEI platforms significantly enhance Developer Productivity by offering a unified view of engineering activities, enabling leaders to make informed decisions. Key benefits include:

Enhanced Visibility

SEI platforms provide a comprehensive view of engineering processes, helping leaders identify inefficiencies and areas for improvement.

Data-Driven Decisions

By collecting and analyzing data from various tools, SEI platforms offer insights that drive smarter business decisions.

Continuous Improvement

Organizations can use insights from SEI platforms to continually adjust and improve their processes, leading to higher quality software and more productive teams. This aligns with IEEE’s emphasis on benchmarking for achieving software engineering excellence.

Industry Benchmarking

SEI platforms enable benchmarking against industry standards, helping teams set realistic goals and measure their progress. This continuous improvement cycle drives sustained productivity gains.

User Experience and Customization

Personalization and customization are critical for SEI platforms, ensuring they meet the specific needs of different user personas. Tailored user experiences lead to higher adoption rates and better user satisfaction, as highlighted by IDC.

Inference

The SEI platform market is poised for significant growth, driven by the need for data-driven insights into software engineering processes. These platforms offer substantial benefits, including enhanced visibility, data-driven decision-making, and continuous improvement. As the market matures, SEI platforms will become indispensable tools for software engineering leaders, helping them demonstrate their teams’ value and drive productivity gains.

Top Representative Players in SEI

SEI

Considering to Implement SEI Recommended DORA Metrics?

Conclusion

SEI platforms represent a transformative opportunity for software engineering organizations. By leveraging these platforms, organizations can gain a competitive edge, delivering higher quality software and achieving better business outcomes. The integration of AI and machine learning further enhances these platforms’ capabilities, providing actionable insights that drive continuous improvement. As adoption increases, SEI platforms will play a crucial role in the future of software engineering, enabling leaders to make data-driven decisions and boost developer productivity.

Sources

  1. Gartner. (2024). “Software Engineering Intelligence Platforms Market Guide”.
  2. McKinsey & Company. (2023). “The State of Developer Productivity“.
  3. DevOps Research and Assessment (DORA). (2023). “Accelerate: State of DevOps Report”.
  4. Forrester Research. (2023). “AI in Software Development: Enhancing Efficiency and Quality”.
  5. IEEE Software. (2023). “Benchmarking for Software Engineering Excellence”.
  6. IDC. (2023). “Personalization in Software Engineering Tools: Driving Adoption and Satisfaction”.

Software Engineering Benchmark Report: Driving Excellence through Metrics

Introduction

In today’s software engineering, the pursuit of excellence hinges on efficiency, quality, and innovation. Engineering metrics, particularly the transformative DORA (DevOps Research and Assessment) metrics, are pivotal in gauging performance. According to the 2023 State of DevOps Report, high-performing teams deploy code 46 times more frequently and are 2,555 times faster from commit to deployment than their low-performing counterparts.

However, true excellence extends beyond DORA metrics. Embracing a variety of metrics—including code quality, test coverage, infrastructure performance, and system reliability—provides a holistic view of team performance. For instance, organizations with mature DevOps practices are 24 times more likely to achieve high code quality, and automated testing can reduce defects by up to 40%.

This benchmark report offers comprehensive insights into these critical metrics, enabling teams to assess performance, set meaningful targets, and drive continuous improvement. Whether you’re a seasoned engineering leader or a budding developer, this report is a valuable resource for achieving excellence in software engineering.

Understanding Benchmark Calculations

Velocity Metrics

Velocity refers to the speed at which software development teams deliver value. The Velocity metrics gauge efficiency and effectiveness in delivering features and responding to user needs. This includes:

  • PR Cycle Time: The time taken from opening a pull request (PR) to merging it. Elite teams achieve <48 hours, while those needing focus take >180 hours.
  • Coding Time: The actual time developers spend coding. Elite teams manage this in <12 hours per PR.
  • Issue Cycle Time: Time taken to resolve issues. Top-performing teams resolve issues in <12 hours.
  • Issue Velocity: Number of issues resolved per week. Elite teams handle >25 issues weekly.
  • Mean Time To Restore: Time taken to restore service after a failure. Elite teams restore services in <1 hour.

Quality Metrics

Quality represents the standard of excellence in development processes and code quality, focusing on reliability, security, and performance. It ensures that products meet user expectations, fostering trust and satisfaction. Quality metrics include:

  • PRs Merged Without Review: Percentage of PRs merged without review. Elite teams keep this <5% to ensure quality.
  • PR Size: Size of PRs in lines of code. Elite teams maintain PRs to <250 lines.
  • Average Commits After PR Raised: Number of commits added after raising a PR. Elite teams keep this <1.
  • Change Failure Rate: Percentage of deployments causing failures. Elite teams maintain this <15%.

Throughput Metrics

Throughput measures the volume of features, tasks, or user stories delivered, reflecting the team’s productivity and efficiency in achieving objectives. Key throughput metrics are:

  • Code Changes: Number of lines of code changed. Elite teams change <100 lines per PR.
  • PRs Created: Number of PRs created per developer. Elite teams average >5 PRs per week per developer.
  • Coding Days: Number of days spent coding. Elite teams achieve this >4 days per week.
  • Merge Frequency: Frequency of PR merges. Elite teams merge >90% of PRs within a day.
  • Deployment Frequency: Frequency of code deployments. Elite teams deploy >1 time per day.

Collaboration Metrics

Collaboration signifies the cooperative effort among software development team members to achieve shared goals. It entails effective communication and collective problem-solving to deliver high-quality software products efficiently. Collaboration metrics include:

  • Time to First Comment: Time taken for the first comment on a PR. Elite teams respond within <6 hours.
  • Merge Time: Time taken to merge a PR after it is raised. Elite teams merge PRs within <4 hours.
  • PRs Reviewed: Number of PRs reviewed per developer. Elite teams review >15 PRs weekly.
  • Review Depth/PR: Number of comments per PR during the review. Elite teams average <5 comments per PR.
  • Review Summary: Overall review metrics summary including depth and speed. Elite teams keep review times and comments to a minimum to ensure efficiency and quality.

Benchmarking Structure

Performance Levels

The benchmarks are organized into the following levels of performance for each metric:

  • Elite – Top 10 Percentile
  • High – Top 30 Percentile
  • Medium – Top 60 Percentile
  • Needs Focus – Bottom 40 Percentile

These levels help teams understand where they stand in comparison to others and identify areas for improvement.

Data Sources

The data in the report is compiled from over 1,500 engineering teams and more than 2 million pull requests across the US, Europe, and Asia. This comprehensive data set ensures that the benchmarks are representative and relevant.

Implementation of Software Engineering Benchmarks

Step-by-Step Guide

  • Identify Key Metrics: Begin by identifying the key metrics that are most relevant to your team’s goals. This includes selecting from velocity, quality, throughput, and collaboration metrics.
  • Collect Data: Use tools like continuous integration/continuous deployment (CI/CD) systems, version control systems, and project management tools to collect data on the identified metrics.
  • Analyze Data: Use statistical methods and tools to analyze the collected data. This involves calculating averages, medians, percentiles, and other relevant statistics.
  • Compare Against Benchmarks: Compare your team’s metrics against industry benchmarks to identify areas of strength and areas needing improvement.
  • Set Targets: Based on the comparison, set realistic and achievable targets for improvement. Aim to move up to the next percentile level for each metric.
  • Implement Improvements: Develop and implement a plan to achieve the set targets. This may involve adopting new practices, tools, or processes.
  • Monitor Progress: Continuously monitor your team’s performance against the set targets and make adjustments as necessary.

Tools and Practices

  • Continuous Integration/Continuous Deployment (CI/CD): Automates the integration and deployment process, ensuring quick and reliable releases.
  • Agile Methodologies: Promotes iterative development, collaboration, and flexibility to adapt to changes.
  • Code Review Tools: Facilitates peer review to maintain high code quality.
  • Automated Testing Tools: Ensures comprehensive test coverage and identifies defects early in the development cycle.
  • Project Management Tools: Helps in tracking progress, managing tasks, and facilitating communication among team members.

Importance of a Metrics Program for Engineering Teams

Performance Measurement and Improvement

Engineering metrics serve as a cornerstone for performance measurement and improvement. By leveraging these metrics, teams can gain deeper insights into their processes and make data-driven decisions. This helps in:

  • Identifying Bottlenecks: Metrics highlight areas where the development process is slowing down, enabling teams to address issues proactively.
  • Measuring Progress: Regularly tracking metrics allows teams to measure their progress towards goals and make necessary adjustments.
  • Improving Efficiency: By focusing on key metrics, teams can streamline their processes and improve efficiency.

Benchmarking Against Industry Standards

Engineering metrics provide a valuable framework for benchmarking performance against industry standards. This helps teams:

  • Set Meaningful Targets: By understanding where they stand in comparison to industry peers, teams can set realistic and achievable targets.
  • Drive Continuous Improvement: Benchmarking fosters a culture of continuous improvement, motivating teams to strive for excellence.
  • Gain Competitive Advantage: Teams that consistently perform well against benchmarks are likely to deliver high-quality products faster, gaining a competitive advantage in the market.

Enhancing Team Collaboration and Communication

Metrics also play a crucial role in enhancing team collaboration and communication. By tracking collaboration metrics, teams can:

  • Identify Communication Gaps: Metrics can reveal areas where communication is lacking, enabling teams to address issues and improve collaboration.
  • Foster Teamwork: Regularly reviewing collaboration metrics encourages team members to work together more effectively.
  • Improve Problem-Solving: Better communication and collaboration lead to more effective problem-solving and decision-making.

Key Actionables

  • Adopt a Metrics Program: Implement a comprehensive metrics program to measure and improve your team’s performance.
  • Benchmark Regularly: Regularly compare your metrics against industry benchmarks to identify areas for improvement.
  • Set Realistic Goals: Based on your benchmarking results, set achievable and meaningful targets for your team.
  • Invest in Tools: Utilize tools like Typo, CI/CD systems, automated testing, and project management software to collect and analyze metrics effectively.
  • Foster a Culture of Improvement: Encourage continuous improvement by regularly reviewing metrics and making necessary adjustments.
  • Enhance Collaboration: Use collaboration metrics to identify and address communication gaps within your team.
  • Learn from High-Performing Teams: Study the practices of high-performing teams to identify strategies that can be adapted to your team.

Conclusion

Delivering quickly isn’t easy. It’s tough dealing with technical challenges and tight deadlines. But leaders in engineering guide their teams well. They encourage creativity and always look for ways to improve. Metrics are like helpful guides. They show us where we’re doing well and where we can do better. With metrics, teams set goals and see how they measure up to others. It’s like having a map to success.

With strong leaders, teamwork, and using metrics wisely, engineering teams can overcome challenges and achieve great things in software engineering. This Software Engineering Benchmarks Report provides valuable insights into their current performance, empowering them to strategize effectively for future success. Predictability is essential for driving significant improvements. A consistent workflow allows teams to make steady progress in the right direction.

By standardizing processes and practices, teams of all sizes can streamline operations and scale effectively. This fosters faster development cycles, streamlined processes, and high-quality code. Typo has saved significant hours and costs for development teams, leading to better quality code and faster deployments.

You can start building your metrics today with Typo for FREE. Our focus is to help teams ship reliable software faster.

To learn more about setting up metrics

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How to Improve your Sprint Review Meeting

Sprint Review Meetings are a cornerstone of Agile and Scrum methodologies, serving as a crucial touchpoint for teams to showcase their progress, gather feedback, and align on the next steps. However, many teams struggle to make the most of these meetings. This blog will explore how to enhance your Sprint Review Meetings to ensure they are effective, engaging, and productive.

What is the Purpose of Sprint Review Meetings?

The Sprint Review Meetings are meant to evaluate the progress made during a sprint, review the completed work, collect stakeholder feedback, and discuss the upcoming sprints. Key participants include the Scrum team, the Product Owner, key stakeholders, and occasionally the Scrum Master.

It’s important to differentiate Sprint Reviews from Sprint Retrospectives. While the former focuses on what was achieved and gathering feedback, the latter centers on process improvements and team dynamics.

Preparation is Key

Preparation can make or break a Sprint Review Meeting. Ensuring that the team is ready involves several steps.

  • Ensure that the sprint review agenda is clear.
  • Ensure that the development team is fully prepared to discuss their individual contributions and any challenges they may have encountered. Everyone needs to be ready to actively participate in the discussion.
  • Set up a demo environment that is stable, accessible, and conducive to effective demonstrations. It’s crucial that the environment is reliable and allows for seamless presentations.
  • Collect and organize all pertinent materials and data, including user stories, acceptance criteria, and metrics that demonstrate progress. Having these resources readily available will help facilitate discussions and provide clarity on the project’s status.

Effective Collaboration and Communication

Encouraging direct collaboration between stakeholders and teams is essential for the success of any project. It is important to create an environment where open communication is not only encouraged but also valued.

This means avoiding the use of excessive technical jargon, which can make non-technical stakeholders feel excluded. Instead, strive to facilitate clear and transparent communication that allows all voices to be heard and valued. Providing a platform for open and honest feedback will ensure that everyone’s perspectives are considered, leading to a more inclusive and effective collaborative process.

Structure and Agenda of a Productive Sprint Review

It is crucial to have a clearly defined agenda for a productive Sprint Review. This includes sharing the agenda well in advance of the meeting, and clearly outlining the main topics of discussion. It’s also important to allocate specific time slots for each segment of the meeting to ensure that the review remains efficient.

The agenda should include discussions on completed work, work that was not completed, and the next steps to be taken. This level of detail and structure helps to ensure that the Sprint Review is focused and productive.

Demonstration of Work Done

When presenting completed work, it’s important to ensure that the demonstration is engaging and interactive. To achieve this, consider the following best practices:

  • Emphasize Value: Focus on the value delivered by the completed work and how it meets the specific needs of stakeholders. Highlighting the positive impact and benefits of the work will help stakeholders understand its significance.
  • Interactive Demos: Encourage stakeholders to actively engage with the product or solution being presented. Providing a hands-on experience can help stakeholders better understand its functionality and benefits. This can be achieved through demonstrations, simulations, or interactive presentations.
  • Outcome-Oriented Approach: Instead of solely focusing on the features of the completed work, emphasize the outcomes and value created. Highlight the tangible results and benefits that have been achieved, making it clear how the work contributes to overall objectives and goals.

By following these best practices, you can ensure that the demonstration of completed work is not only informative but also compelling and impactful for stakeholders.

Gathering and Incorporating Feedback

Effective feedback collection is crucial for continuous improvement:

  • Eliciting Constructive Feedback: Use techniques like open-ended questions to draw out detailed responses.
  • Active Listening: Show stakeholders their feedback is valued and taken seriously.
  • Documenting Feedback: Record feedback systematically and ensure it is actionable and prioritized for future sprints.

Questions to Ask During the Sprint Review Meeting?

The Sprint Review Meeting is an important collaborative meeting where team members, engineering leaders, and stakeholders can review previous and discuss key pointers. Below are a few questions that need to be asked during this review meeting:

Product Review

  • What was accomplished during the sprint?
  • Are there any items that need to be completed? Why wasn’t it finished?
  • How does the completed work align with the sprint goal?
  • Were there any unexpected challenges or obstacles that arose?

Team Performance

  • Did the team meet the sprint goal? If not, why?
  • What went well during this sprint?
  • What didn’t go well during this sprint?
  • Were there any bottlenecks or challenges that affected productivity?

Planning for the Next Sprint

  • What are the priorities for the next sprint?
  • Are there any new user stories or tasks that must be added to the backlog?
  • What are the critical tasks that must be completed in the next sprint?
  • How should we address any carry-over work from this sprint?

Using Tools and Technology Effectively

Use collaborative tools to improve the review process:

  • Collaborative Tools: Tools such as Typo can help facilitate interactive and visual discussions.
  • Visual Aids: Incorporate charts, graphs, and other visual aids to make data more accessible.
  • Record Sessions: Think about recording the session for those unable to attend and for future reference.

How Typo can Enhance your Sprint Review Meeting?

Typo is a collaborative tool designed to enhance the efficiency and effectiveness of team meetings, including Sprint Review Meetings. Our sprint analysis feature uses data from Git and issue management tools to provide insights into how your team is working. You can see how long tasks take, how often they’re blocked, and where bottlenecks occur. It allows to track and analyze the team’s progress throughout a sprint and provides valuable insights into work progress, work breakup, team velocity, developer workload, and issue cycle time. This information can help you identify areas for improvement and ensure your team is on track to meet their goals.

Key Components of Sprint Analysis Tool

Work Progress

Work progress represents the percentage breakdown of issue tickets or story points in the selected sprint according to their current workflow status.

Work Breakup

Work breakup represents the percentage breakdown of issue tickets in the current sprint according to their issue type or labels.

Screenshot 2024-03-16 at 12.03.44 AM.png

Team Velocity

Team Velocity represents the average number of completed issue tickets or story points across each sprint.

Developer Workload

Developer workload represents the count of issue tickets or story points completed by each developer against the total issue tickets/story points assigned to them in the current sprint.

Issue Cycle Time

Issue cycle time represents the average time it takes for an issue ticket to transition from the ‘In Progress’ state to the ‘Completion’ state.

Scope Creep

Scope creep is one of the common project management risks. It represents the new project requirements that are added to a project beyond what was originally planned.

Screenshot 2024-03-16 at 12.06.28 AM.png

Here’s how Typo can be used to improve Sprint Review Meetings:

Agenda Setting and Sharing

Typo allows you to create and share detailed agendas with all meeting participants ahead of time. For Sprint Review Meetings, you can outline the key elements such as:

  • Review of completed work
  • Demonstration of new features
  • Feedback session
  • Planning next steps

Sharing the agenda in advance ensures everyone knows what to expect and can prepare accordingly.

Real-Time Collaboration

Typo enhances sprint review meetings by providing real-time collaboration capabilities and comprehensive metrics. Live data access and interactive dashboards ensure everyone has the most current information and can engage in dynamic discussions. Key metrics such as velocity, issue tracking, and cycle time provide valuable insights into team performance and workflow efficiency. This transparency and data-driven approach facilitate informed decision-making, improve accountability, and support continuous improvement, making sprint reviews more productive and collaborative.

Feedback Collection and Management

Typo makes it easy to collect, organize, and prioritize valuable feedback. Users can utilize feedback forms or surveys integrated within Typo to gather structured feedback from stakeholders. The platform allows for real-time documentation of feedback, ensuring that no valuable insights are lost. Additionally, users can categorize and tag feedback for easier tracking and action planning.

Visual Aids and Presentation Tools

Use Typo’s presentation tools to enhance the demonstration of completed work. Incorporate charts, graphs, and other visual aids to make the progress more understandable and engaging. Use interactive elements to allow stakeholders to explore the new features hands-on.

Continuous Improvement

In Sprint Review Meetings, Typo can be used to drive continuous improvement by analyzing feedback trends, identifying recurring issues or areas for improvement, encouraging team members to reflect on past meetings and suggest enhancements, and implementing data-driven insights to make each Sprint Review more effective than the last.

Improve your Sprint Review Meetings with the Right Steps

A well-executed Sprint Review Meeting can significantly enhance your team’s productivity and alignment with stakeholders. By focusing on preparation, effective communication, structured agendas, interactive demos, and continuous improvement, you can transform your Sprint Reviews into a powerful tool for success. Clear goals should be established at the outset of each meeting to provide direction and focus for the team.

Remember, the key is to foster a collaborative environment where valuable feedback is provided and acted upon, driving your team toward continuous improvement and excellence. Integrating tools like Typo can provide the structure and capabilities needed to elevate your Sprint Review Meetings, ensuring they are both efficient and impactful.

Top 6 LinearB Alternatives

Software engineering teams are crucial for the organization. They build high-quality products, gather and analyze requirements, design system architecture and components, and write clean, efficient code. Hence, they are the key drivers of success.

Measuring their success and considering if they are facing any challenges is important. And that’s how Engineering Analytics Tools comes to the rescue. One of the popular tools is LinearB which engineering leaders and CTOs across the globe have widely used.

While this is usually the best choice for the organizations, there might be chances that it doesn’t work for you. Worry not! We’ve curated the top 6 LinearB alternatives that you can take note of when considering engineering analytics tools for your company.

What is LinearB?

LinearB is a well-known software engineering analytics platform that measures GIT data, tracks DORA metrics, and collects data from other tools. By combining visibility and automation, it enhances operational efficiency and provides a comprehensive view of performance. Its project delivery forecasting and goal-setting features help engineering leaders stay on schedule and monitor team efficiency. LinearB can be integrated with Slack, JIRA, and popular CI/CD tools. However, LinearB has limited features to support the SPACE framework and individual performance insights.

LinearB Alternatives

Besides LinearB, there are other leading alternatives as well. Take a look below:

Typo

Typo is another popular software engineering intelligence platform that offers SDLC visibility, developer insights, and workflow automation for building high-performing tech teams. It can be seamlessly integrated into the tech tools stack including the GIT version (GitHub, GitLab), issue tracker (Jira, Linear), and CI/CD (Jenkins, CircleCI) tools to ensure a smooth data flow. Typo also offers comprehensive insights into the deployment process through key DORA and other engineering metrics. With its automated code tool, the engineering team can identify code issues and auto-fix them before merging to master.

Features

  • DORA and other engineering metrics can be configured in a single dashboard.
  • Captures a 360-degree view of developers’ experience i.e. qualitative insights and an in-depth view of the real issues.
  • Offers engineering benchmark to compare the team’s results across industries.
  • Effective sprint analysis tracks and analyzes the team’s progress throughout a sprint.
  • Reliable and prompt customer support.

Jellyfish

Jellyfish is a leading GIT tracking tool for tracking metrics by aligning engineering insights with business goals. It analyzes the activities of engineers in a development and management tool and provides a complete understanding of the product. Jellyfish shows the status of every pull request and offers relevant information about the commit that affects the branch. It can be easily integrated with JIRA, Bitbucket, Gitlab, and Confluence.

Features

  • Provides multiple views on resource allocation.
  • Real-time visibility into engineering organization and team progress.
  • Provides you access to benchmarking data on engineering metrics.
  • Includes DevOps metrics for continuous delivery.
  • Transforms data into reports and insights for both management and leadership.

Swarmia

Swarmia is a popular tool that offers visibility across three crucial areas: business outcome, developer productivity, and developer experience. It provides quantitative insights into the development pipeline. It helps the team identify initiatives falling behind their planned schedule by displaying the impact of unplanned work, scope creep, and technical debt. Swarmia can be integrated with tech tools like source code hosting, issue trackers, and chat systems.

Features

  • Investment balance gives insights into the purpose of each action and money spent by the company on each category.
  • User-friendly dashboard.
  • Working agreement features include 20+ work agreements used by the industry’s top-performing teams.
  • Tracks healthy software engineering measures such as DORA metrics.
  • Automation feature allows all tasks to be assigned to the appropriate issues and persons.

Waydev

Waydev is a software development analytics platform that uses an agile method for tracking output during the development process. It puts more stress on market-based metrics and gives cost and progress of delivery and key initiatives. Its flexible reporting allows for building complex custom reports. Waydev can be seamlessly integrated with Gitlab, Github, CircleCI, AzureOPS, and other well-known tools.

Features

  • Provides automated insights on metrics related to bug fixes, velocity, and more.
  • Easy to digest.
  • Allows engineering leaders to see data from different perspectives.
  • Creates custom goals, targets, or alerts.
  • Offers budgeting reports for engineering leaders.

Waydev Updates: Custom Dashboards & Benchmarking - Waydev

Pluralsight Flow

Pluralsight Flow provides a detailed overview of the development process and helps identify friction and bottlenecks in the development pipeline. It tracks DORA metrics, software development KPIs, and investment insights which allows for aligning engineering efforts with strategic objectives. Pluralsight Flow can be integrated with various manual and automated testing tools such as Azure DevOps, and GitLab.

Features

  • Offers insights into why trends occur and what could be the related issues.
  • Predicts value impact for project and process proposals.
  • Features DORA analytics and investment insights.
  • Provides centralized insights and data visualization for data sharing and collaboration.
  • Easy to manage configuration.

Sleuth

Sleuth assists development teams in tracking and improving DORA metrics. It provides a complete picture of existing and planned deployments as well as the effect of releases. Sleuth gives teams visibility and actionable insights on efficiency and can be integrated with AWS CloudWatch, Jenkins, JIRA, Slack, and many more.

Features

  • Provides automated and easy deployment process.
  • Keeps team up to date on how they are performing against their goal over time.
  • Automatically suggests efficiency goals based on teams’ historical metrics.
  • Lightweight and adaptable.
  • Accurate picture of software development performance and provides insights.

Conclusion

Software development analytics tools are important for keeping track of project pipelines and measuring developers’ productivity. It allows engineering managers to gain visibility into the dev team performance through in-depth insights and reports.

Take the time to conduct thorough research before selecting any analytics tool. It must align with your team’s needs and specifications, facilitate continuous improvement, and integrate with your existing and forthcoming tech tools.

All the best!

Understanding DORA Metrics: Cycle Time vs Lead Time in Software Development

In the dynamic world of software development, where speed and quality are paramount, measuring efficiency is critical. DevOps Research and Assessment (DORA) metrics provide a valuable framework for gauging the performance of software development teams. Two of the most crucial DORA metrics are cycle time and lead time. This blog post will delve into these metrics, explaining their definitions, differences, and significance in optimizing software development processes. To start with, here’s the most simple explanation of the two metrics –

What is Lead Time?

Lead time refers to the total time it takes to deliver a feature or code change to production, from the moment it’s first conceived as a user story or feature request. In simpler terms, it’s the entire journey of a feature, encompassing various stages like:

  • Initiating a user story or feature request: This involves capturing the user’s needs and translating them into a clear and concise user story or feature request within the backlog.
  • Development and coding: Once prioritized, the development team works on building the feature, translating the user story into functional code.
  • Testing and quality assurance: Rigorous testing ensures the feature functions as intended and meets quality standards. This may involve unit testing, integration testing, and user acceptance testing (UAT).
  • Deployment to production: The final stage involves deploying the feature to production, making it available to end users.

What is Cycle Time?

Cycle time, on the other hand, focuses specifically on the development stage. It measures the average time it takes for a developer’s code to go from being committed to the codebase to being PR merged. Unlike lead time, which considers the entire delivery pipeline, cycle time is an internal metric that reflects the development team’s efficiency. Here’s a deeper dive into the stages that contribute to cycle time:

  • The “Coding” stage represents the time taken by developers to write and complete the code changes.
  • The “Pickup” stage denotes the time spent before a pull request is assigned for review.
  • The “Review” stage encompasses the time taken for peer review and feedback on the pull request.
  • Finally, the “Merge” stage shows the duration from the approval of the pull request to its integration into the main codebase.

Screenshot 2024-03-16 at 1.14.10 AM.png
Wanna Measure Cycle Time, Lead Time & Other Critical SDLC Metrics for your Team?

Key Differences between Lead Time and Cycle Time

Here’s a table summarizing the key distinctions between lead time and cycle time, along with additional pointers to consider for a more nuanced understanding:

Category

Lead Time

Cycle Time

Focus

Entire delivery pipeline

Development stage

Influencing Factors

– Feature complexity (design, planning, testing) 

– Prioritization decisions (backlog management) 

– External approvals (design, marketing) – External dependencies (APIs, integrations) 

– Waiting for infrastructure provisioning

– Developer availability 

– Code quality issues (code reviews, bug fixes) 

– Development tooling and infrastructure maturity (build times, deployment automation)

Variability

Higher variability due to external factors

Lower variability due to focus on internal processes

Actionable Insights

Requires further investigation to pinpoint delays (specific stage analysis)

Provides more direct insights for development team improvement (code review efficiency, build optimization)

Metrics Used

– Time in backlog 

– Time in design/planning 

– Time in development 

– Time in testing (unit, integration, UAT) – Deployment lead time

– Coding time

– Code review time 

– Merge time

Improvement Strategies

– Backlog refinement and prioritization – Collaboration with stakeholders for faster approvals 

– Manage external dependencies effectively 

– Optimize infrastructure provisioning processes

– Improve developer skills and availability 

– Implement code review best practices 

– Automate build and deployment processes

Scenario: Implementing a Login with Social Media Integration Feature

Imagine a software development team working on a new feature: allowing users to log in with their social media accounts. Let’s calculate the lead time and cycle time for this feature.

Lead Time (Total Time)

  • User Story Creation (1 Day): A product manager drafts a user story outlining the login with social media functionality.
  • Estimation & Backlog (2 Days): The development team discusses the complexity, estimates the effort (in days) to complete the feature, and adds it to the product backlog.
  • Development & Testing (5 Days): Once prioritized, developers start coding, implementing the social media login functionality, and writing unit tests.
  • Code Review & Merge (1 Day): A code review is conducted, feedback is addressed, and the code is merged into the main branch.
  • Deployment & Release (1 Day): The code is deployed to a staging environment, tested thoroughly, and finally released to production.

Lead Time Calculation

Lead Time = User Story Creation + Estimation + Development & Testing + Code Review & Merge + Deployment & Release Lead Time = 1 Day + 2 Days + 5 Days + 1 Day + 1 Day Lead Time = 10 Days

Cycle Time (Development Focused Time)

This considers only the time the development team actively worked on the feature (excluding waiting periods).

  • Coding (3 Days): The actual time developers spent writing and testing the code for the social media login functionality.
  • Code Review (1 Day): The time taken for the code reviewer to analyze and provide feedback.

Cycle Time Calculation

Cycle Time = Coding + Code Review Cycle Time = 3 Days + 1 Day Cycle Time = 4 Days

Breakdown:

  • Lead Time (10 Days): This represents the entire time from initial idea to the feature being available to users.
  • Cycle Time (4 Days): This reflects the development team’s internal efficiency in completing the feature once they started working on it.

By monitoring and analyzing both lead time and cycle time, the development team can identify areas for improvement. Reducing lead time could involve streamlining the user story creation or backlog management process. Lowering cycle time might suggest implementing pair programming for faster collaboration or optimizing the code review process.

Optimizing Lead Time and Cycle Time: A Strategic Approach

By understanding the distinct roles of lead time and cycle time, development teams can implement targeted strategies for improvement:

Lead Time Reduction

  • Backlog Refinement: Regularly prioritize and refine the backlog, ensuring user stories are clear, concise, and ready for development.
  • Collaboration and Communication: Foster seamless communication between developers, product owners, and other stakeholders to avoid delays and rework caused by misunderstandings.
  • Streamlined Approvals: Implement efficient approval processes for user stories and code changes to minimize bottlenecks.
  • Dependency Management: Proactively identify and address dependencies on external teams or resources to prevent delays.

Cycle Time Reduction

  • Continuous Integration and Continuous Delivery (CI/CD): Automate testing and deployment processes using CI/CD pipelines to expedite code delivery to production.
  • Pair Programming: Encourage pair programming sessions to promote knowledge sharing, improve code quality, and identify bugs early in the development cycle.
  • Code Reviews: Implement efficient code review practices to catch potential issues and ensure code adheres to quality standards.
  • Focus on Work in Progress (WIP) Limits: Limit the number of concurrent tasks per developer to minimize context switching and improve focus.
  • Invest in Developer Tools and Training: Equip developers with the latest tools and training opportunities to enhance their development efficiency and knowledge.

The synergy of Lead Time and Cycle Time

Lead time and cycle time, while distinct concepts, are not mutually exclusive. Optimizing one metric ultimately influences the other. By focusing on lead time reduction strategies, teams can streamline the overall development process, leading to shorter cycle times. Consequently, improving development efficiency through cycle time reduction translates to faster feature delivery, ultimately decreasing lead time. This synergistic relationship highlights the importance of tracking and analyzing both metrics to gain a holistic view of software delivery performance.

Leveraging DORA metrics for Continuous Improvement

Lead time and cycle time are fundamental DORA metrics that provide valuable insights into software development efficiency and customer experience. By understanding their distinctions and implementing targeted improvement strategies, development teams can optimize their workflows and deliver high-quality features faster.

This data-driven approach, empowered by DORA metrics, is crucial for achieving continuous improvement in the fast-paced world of software development. Remember, DORA metrics extend beyond lead time and cycle time. Deployment frequency and change failure rate are additional metrics that offer valuable insights into the software delivery pipeline’s health. By tracking a comprehensive set of DORA metrics, development teams can gain a holistic view of their software delivery performance and identify areas for improvement across the entire value stream.

This empowers teams to:

  • Increase software delivery velocity by streamlining development processes and accelerating feature deployment.
  • Enhance software quality and reliability by implementing robust testing practices and reducing the likelihood of bugs in production.
  • Reduce development costs through efficient resource allocation, minimized rework, and faster time-to-market.
  • Elevate customer satisfaction by delivering features faster and responding to feedback more promptly.

By evaluating all these DORA metrics holistically, development teams gain a comprehensive understanding of their software development performance. This allows them to identify areas for improvement across the entire delivery pipeline, leading to faster deployments, higher quality software, and ultimately, happier customers.

Wanna Improve your Dev Productivity with DORA Metrics?

8 must-have software engineering meetings

Software developers have a lot on their plate. Attending too many meetings and that too without any agenda can be overwhelming for them.

The meetings must be with a purpose, help the engineering team to make progress, and provide an opportunity to align their goals, priorities, and expectations.

Below are eight important software engineering meetings you should conduct timely.

Must-have software engineering meetings

There are various types of software engineering meetings. We’ve curated a list of must-have engineering meetings along with a set of metrics.

These metrics serve to provide structure and outcomes for the software engineering meetings. Make sure to ask the right questions with a focus on enhancing team efficiency and align the discussions with measurable metrics.

Daily standups

Such types of meetings happen daily. These are short meetings that typically occur for 15 minutes or less. Daily standup meetings focus on four questions:

  • How is everyone on the team progressing towards their goals?
  • Is everyone on the same page?
  • Are there any challenges or blockers for individual team members?

It allows software developers to have a clear, concise agenda and focus on the same goal. Moreover, it helps in avoiding duplication of work and prevents wasting time and effort.

Metrics for daily standups

Check-ins

These include the questions around inspection, transparency, adaption, and blockers (mentioned above), hence, simplifying the check-in process. It allows team members to understand each others’ updates and track progress over time. This allows standups to remain relevant and productive.

Daily activity

Daily activity promotes a robust, continuous delivery workflow by ensuring the active participation of every engineer in the development process. This metric includes a range of symbols that represent various PR activities of the team’s work such as Commit, Pull Request, PR Merge, Review, and Comment. It further gives valuable information including the type of Git activity, the name and number of the PR, changes in the line of code in this PR, the repository name where this PR lies, and so on.

Daily activity
Work in progress

Work progress helps in understanding what teams are working on and objective measures of their work progress. This allows engineering leaders and developers to better plan for the day, identify blockers in the early stages, and think critically about the progress.

Work in progress

Sprint planning meetings

Sprint planning meetings are conducted at the beginning of each sprint. It allows the scrum team to decide what work will they complete in the upcoming iteration, set sprint goals, and align on the next steps. The key purpose of these meetings is for the team to consider how will they approach doing what the product owner has requested.

These plannings are done based on the velocity or capacity and the sprint length.

Metrics for sprint planning meetings

Sprint goals

Sprint goals are the clear, concise objectives the team aims to achieve during the sprint. It helps the team understand what they need to achieve and ensure everyone is on the same page and working towards a common goal.

These are set based on the previous velocity, cycle time, lead time, work-in-progress, and other quality metrics such as defect counts and test coverage.

Sprint - carry over

It represents the Issues/Story Points that were not completed in the sprint and moved to later sprints. Monitoring carry-over items during these meetings allows teams to assess their sprint planning accuracy and execution efficiency. It also enables teams to uncover underlying reasons for incomplete work which further helps identify the root causes to address them effectively.

Developer workload
Developer workload

Developer Workload represents the count of Issue tickets or Story points completed by each developer against the total Issue tickets/Story points assigned to them in the current sprint. Keeping track of developer workload is essential as it helps in informed decision-making, efficient resource management, and successful sprint execution in agile software development.

Planning accuracy

Planning Accuracy represents the percentage of Tasks Planned versus Tasks Completed within a given time frame. Measuring planning accuracy helps identify discrepancies between planned and completed tasks which further helps in better allocating resources and manpower to tasks. It also enables a better estimate of the time required for tasks, leading to improved time management and more realistic project timelines.

Planning accuracy

Weekly priority meetings

Such types of meetings work very well with sprint planning meetings. These are conducted at every start of the week (Or can be conducted as per the software engineering teams). It helps ensure a smooth process and the next sprint lines up with the team’s requirements to be successful. These meetings help to prioritize tasks, goals, and objectives for the week, what was accomplished in the previous week, and what needs to be done in the upcoming week. This helps align, collaborate, and plan among team members.

Metrics for weekly priority meetings

Sprint progress

Sprint progress helps the team understand how they are progressing toward their sprint goals and whether any adjustments are needed to stay on track. Some of the common metrics for sprint progress include:

  • Team velocity
  • Sprint burndown chart
  • Daily standup updates
  • Work progress and work breakup
Code health

Code health provides insights into the overall quality and maintainability of the codebase. Monitoring code health metrics such as code coverage, cyclomatic complexity, and code duplication helps identify areas needing refactoring or improvement. It also offers an opportunity for knowledge sharing and collaboration among team members.

Code health
PR activity

Analyzing pull requests by a team through different data cuts can provide valuable insights into the engineering process, team performance, and potential areas for improvement. Software engineers must follow best dev practices aligned with improvement goals and impact software delivery metrics. Engineering leaders can set specific objectives or targets regarding PR activity for tech teams. It helps to track progress towards these goals, provides insights on performance, and enables alignment with the best practices to make the team more efficient.

PR activity
Deployment frequency

Deployment frequency measures how often code is deployed into production per week, taking into account everything from bug fixes and capability improvements to new features. Measuring deployment frequency offers in-depth insights into the efficiency, reliability, and maturity of an engineering team’s development and deployment processes. These insights can be used to optimize workflows, improve team collaboration, and enhance overall productivity.

Deployment frequency

Performance review meetings

Performance review meetings help in evaluating engineering works during a specific period. These meetings can be conducted biweekly, monthly, quarterly, and annually. These effective meetings help individual engineers understand their weaknesses, and strengths and improve their work. Engineering managers can provide constructive feedback to them, offer guidance accordingly, and provide growth opportunities.  

Metrics for performance review meetings

Code coverage

It measures the percentage of code that is executed by automated tests offers insight into the effectiveness of the testing strategy and helps ensure that critical parts of the codebase are adequately tested. Evaluating code coverage in performance reviews provides insight into the developer’s commitment to producing high-quality, reliable code.

Code coverage
Pull requests

By reviewing PRs in performance review meetings, engineering managers can assess the code quality written by individuals. They can evaluate factors such as adherence to coding standards, best practices, readability, and maintainability. Engineering managers can identify trends and patterns that may indicate areas where developers are struggling to break down tasks effectively.

Pull requests
Developer experience

By measuring developer experience in performance reviews, engineering managers can assess the strengths and weaknesses of a developer’s skill set, and understanding and addressing the aspects can lead to higher productivity, reduced burnout, and increased overall team performance.

Developer experience

Technical meeting

Technical meetings are important for software developers and are held throughout the software product life cycle. In such types of meetings, complex software development tasks are carried out, and discuss the best way to solve an issue.

Technical meetings contain three main stages:

  • Identifying tech issues and concerns related to the project.
  • Asking senior software engineers and developers for advice on tech problems.
  • Finding the best solution for technical problems.

Metrics for technical meeting

Bugs rate

The Bugs Rate represents the average number of bugs raised against the total issues completed for a selected time range. This helps assess code quality and identify areas that require improvement. By actively monitoring and managing bug rates, engineering teams can deliver more reliable and robust software solutions that meet or exceed customer expectations.

Bugs rate
Incident opened

It represents the number of production incidents that occurred during the selected period. This helps to evaluate the business impact on customers and resolve their issues faster. Tracking incidents allows teams to detect issues early, identify the root causes of problems, and proactively identify trends and patterns.

Incident opened
Time to build

Time to Build represents the average time taken by all the steps of each deployment to complete in the production environment. Tracking time to build enables teams to optimize build pipelines, reduce build times, and ensure that teams meet service level agreements (SLAs) for deploying changes, maintaining reliability, and meeting customer expectations.

Time to build
Mean time to restore

Mean Time to Restore (MTTR) represents the average time taken to resolve a production failure/incident and restore normal system functionality each week. MTTR reflects the team’s ability to detect, diagnose, and resolve incidents promptly, identifies recurrent or complex issues that require root cause analysis, and allows teams to evaluate the effectiveness of process improvements and incident management practices.

Sprint retrospective meetings

Sprint retrospective meetings play an important role in agile methodology. Usually, the sprints are two weeks long. These are conducted after the review meeting and before the sprint planning meeting. In these types of meetings, the team discusses what went well in the sprint and what could be improved.

In sprint retrospective meetings, the entire team i.e. developers, scrum master, and the product owner are present. This encourages open discussions and exchange learning with each other.

Metrics for sprint retrospective meetings

Issue cycle time

Issue Cycle Time represents the average time it takes for an Issue ticket to transition from the ‘In Progress’ state to the ‘Completion’ state. Tracking issue cycle time is essential as it provides actionable insights for process improvement, planning, and performance monitoring during sprint retrospective meetings. It further helps in pinpointing areas of improvement, identifying areas for workflow optimization, and setting realistic expectations.

Team velocity

Team Velocity represents the average number of completed Issue tickets or Story points across each sprint. It provides valuable insights into the pace at which the team is completing work and delivering value such as how much work is completed, carry over, and if there’s any scope creep. It helps in assessing the team’s productivity and efficiency during sprints, allowing teams to detect and address these issues early on and offer them constructive feedback by continuously tracking them.

Team velocity
Work in progress

It represents the percentage breakdown of Issue tickets or Story points in the selected sprint according to their current workflow status. Tracking work in progress helps software engineering teams gain visibility into the status of individual tasks or stories within the sprint. It also helps identify bottlenecks or blockers in the workflow, streamline workflows, and eliminate unnecessary handoffs.

Throughput

Throughput is a measure of how many units of information a system can process in a given amount of time. It is about keeping track of how much work is getting done in a specific period. This overall throughput can be measured by

  • The rate at which the Pull Requests are merged into any of the code branches per day.
  • The average number of days per week each developer commits their code to Git.
  • The breakup of total Pull Requests created in the selected time.
  • The average number of Pull Requests merged in the main/master/production branch per week.

Throughput directly reflects the team’s productivity i.e. whether it is increasing, decreasing, or is constant throughout the sprint. It also evaluates the impact of process changes, sets realistic goals, and fosters a culture of continuous improvement.

Work in progress

CTO leadership meeting

These are strategic gatherings that involve the CTO and other key leaders within the tech department. The key purpose of these meetings is to discuss and make decisions on strategic and operations issues related to organizations’ tech initiatives. It allows CTOs and tech leaders to align tech strategy with overall business strategy for setting long-term goals, tech roadmaps, and innovative initiatives.

Besides this, KPIs and other engineering metrics are also reviewed to assess the permanence, measure success, identify blind spots, and make data-driven decisions.

Metrics for CTO leadership meeting

Investment and resource distribution

It is the allocation of time, money, and effort across different work categories or projects for a given time. It helps in optimizing resource allocation and drives dev efforts towards areas of maximum business impact. These insights can further be used to evaluate project feasibility, resource requirements, and potential risks. Hence, allocating the engineering team better to drive maximum deliveries.

Investment and resource distribution
DORA metrics

Measuring DORA metrics is vital for CTO leadership meetings because they provide valuable insights into the effectiveness and efficiency of the software development and delivery processes within the organization. It allows organizations to benchmark their software delivery performance against industry standards and assess how quickly their teams can respond to market changes and deliver value to customers.

DORA metrics
Devex score

DevEx scores directly correlate with developer productivity. A positive DevEx contributes to the achievement of broader business goals, such as increased revenue, market share, and customer satisfaction. Moreover, CTOs and leaders who prioritize DevEx can differentiate their organization as an employer of choice for top technical talent.

One-on-one meetings

In such types of meetings, individuals can have private time with the manager to discuss their challenges, goals, and career progress. They can share their opinion and exchange feedback on various aspects of the work.

Moreover, to create a good working relationship, one-on-one meetings are an essential part of the organization. It allows engineering managers to understand how every team member is feeling at the workplace, setting goals, and discussing concerns regarding their current role.

Metrics are not necessary for one-on-one meetings. While engineering managers can consider the DevEx score and past feedback, their primary focus must be building stronger relationships with their team members, beyond work-related topics.

  • Such meetings must concentrate on the individual’s personal growth, challenges, and career aspirations. Discussing metrics can shift the focus from personal development to performance evaluation, which might not be the primary goal of these meetings.
  • Focusing on metrics during one-on-one meetings can create a formal and potentially intimidating atmosphere. The developer might feel judged and less likely to share honest feedback or discuss personal concerns.
  • One-on-one meetings are an opportunity to discuss the softer aspects of performance that are crucial for a well-rounded evaluation.
  • These meetings are a chance for developers to voice any obstacles or issues they are facing. The engineering leader can then provide support or resources to help overcome these challenges.
  • Individuals may have new ideas or suggestions for process improvements that don’t necessarily fit within the current metrics. Providing a space for these discussions can foster innovation and continuous improvement.

Conclusion

While working on software development projects is crucial, it is also important to have the right set of meetings to ensure that the team is productive and efficient. These software engineering meetings along with metrics empower teams to make informed decisions, allocate tasks efficiently, meet deadlines, and appropriately allocate resources.

Strengthening strategic assumptions with engineering benchmarks

Success in dynamic engineering depends largely on the strength of strategic assumptions. These assumptions serve as guiding principles, influencing decision-making and shaping the trajectory of projects. However, creating robust strategic assumptions requires more than intuition. It demands a comprehensive understanding of the project landscape, potential risks, and future challenges. That’s where engineering benchmarks come in: they are invaluable tools that illuminate the path to success.

Understanding engineering benchmarks

Engineering benchmarks serve as signposts along the project development journey. They offer critical insights into industry standards, best practices, and competitors’ performance. By comparing project metrics against these benchmarks, engineering teams understand where they stand in the grand scheme. From efficiency and performance to quality and safety, benchmarking provides a comprehensive framework for evaluation and improvement.

Benefits of engineering benchmarks

Engineering benchmarks offer many benefits. This includes:

Identify areas of improvement

Areas that need improvement can be identified by comparing performance against benchmarks. Hence, enabling targeted efforts to enhance efficiency and effectiveness.

Decision making

It provides crucial insights for informed decision-making. Therefore, allowing engineering leaders to make data-driven decisions to drive organizational success.

Risk management

Engineering benchmarks help risk management by highlighting areas where performance deviates significantly from established standards or norms.

Change management

Engineering benchmarks provide a baseline against which to measure current performance which helps in effectively tracking progress and monitoring performance metrics before, during, and after implementing changes.

The role of strategic assumptions in engineering projects

Strategic assumptions are the collaborative groundwork for engineering projects, providing a blueprint for decision-making, resource allocation, and performance evaluation. Whether goal setting, creating project timelines, allocating budgets, or identifying potential risks, strategic assumptions inform every aspect of project planning and execution. With a solid foundation of strategic assumptions, projects can avoid veering off course and failing to achieve their objectives. By working together to build these assumptions, teams can ensure a unified and successful project execution.

Identifying gaps in your engineering project

No matter how well-planned, every project can encounter flaws and shortcomings that can impede progress or hinder the project’s success. These flaws can take many forms, such as process inefficiencies, performance deficiencies, or resource utilization gaps. Identifying these areas for improvement is essential for ensuring project success and maintaining strategic direction. By recognizing and addressing these gaps early on, engineering teams can take proactive steps to optimize their processes, allocate resources more effectively, and overcome challenges that may arise during project execution, demonstrating problem-solving capabilities in alignment with strategic direction. This can ultimately pave the way for smoother project delivery and better outcomes.

Leveraging engineering benchmarks to fill gaps

Benchmarking is an essential tool for project management. They enable teams to identify gaps and deficiencies in their projects and develop a roadmap to address them. By analyzing benchmark data, teams can identify improvement areas, set performance targets, and track progress over time.

This continuous improvement can lead to enhanced processes, better quality control, and improved resource utilization. Engineering benchmarks provide valuable and actionable insights that enable teams to make informed decisions and drive tangible results. Access to accurate and reliable benchmark data allows engineering teams to optimize their projects and achieve their goals more effectively.

Building stronger strategic assumptions

Incorporating engineering benchmarks in developing strategic assumptions can play a pivotal role in enhancing project planning and execution, fostering strategic alignment within the team. By utilizing benchmark data, the engineering team can effectively validate assumptions, pinpoint potential risks, and make more informed decisions, thereby contributing to strategic planning efforts.

Continuous monitoring and adjustment based on benchmark data help ensure that strategic assumptions remain relevant and effective throughout the project lifecycle, leading to better outcomes. This approach also enables teams to identify deviations early on and take necessary corrective actions before escalating into bigger issues. Moreover, using benchmark data provides teams with a comprehensive understanding of industry standards, best practices, and trends, aiding in strategic planning and alignment.

Integrating engineering benchmarks into the project planning process helps team members make more informed decisions, mitigate risks, and ensure project success while maintaining strategic alignment with organizational goals.

Key drivers of change and their impact on assumptions

Understanding the key drivers of change is paramount to successfully navigating the ever-shifting landscape of engineering. Technological advancements, market trends, customer satisfaction, and regulatory shifts are among the primary forces reshaping the industry, each exerting a profound influence on project assumptions and outcomes.

Technological advancements

Technological progress is the driving force behind innovation in engineering. From materials science breakthroughs to automation and artificial intelligence advancements, emerging technologies can revolutionize project methodologies and outcomes. By staying abreast of these developments and anticipating their implications, engineering teams can leverage technology to their advantage, driving efficiency, enhancing performance, and unlocking new possibilities.

Market trends

The marketplace is constantly in flux, shaped by consumer preferences, economic conditions, and global events. Understanding market trends is essential for aligning project assumptions with the realities of supply and demand, encompassing a wide range of factors. Whether identifying emerging markets, responding to shifting consumer preferences, or capitalizing on industry trends, engineering teams must conduct proper market research and remain agile and adaptable to thrive in a competitive landscape.

Regulatory changes

Regulatory frameworks play a critical role in shaping the parameters within which engineering projects operate. Changes in legislation, environmental regulations, and industry standards can have far-reaching implications for project assumptions and requirements. Engineering teams can ensure compliance, mitigate risks, and avoid costly delays or setbacks by staying vigilant and proactive in monitoring regulatory developments.

Customer satisfaction

Engineering projects aim to deliver products, services, or solutions that meet the needs and expectations of end-users. Understanding customer satisfaction provides valuable insights into how well engineering endeavors fulfill these requirements. Moreover, satisfied customers are likely to become loyal advocates for a company’s products or services. Hence, by prioritizing customer satisfaction, engineering org can differentiate their offerings in the market and gain a competitive advantage.

Impact on assumptions

The impact of these key drivers of change on project assumptions cannot be overstated. Failure to anticipate technological shifts, market trends, or regulatory changes can lead to flawed assumptions and misguided strategies. By considering these drivers when formulating strategic assumptions, engineering teams can proactively adapt to evolving circumstances, identify new opportunities, and mitigate potential risks. This proactive approach enhances project resilience and positions teams for success in an ever-changing landscape.

Maximizing engineering efficiency through benchmarking

Efficiency is the lifeblood of engineering projects, and benchmarking is a key tool for maximizing efficiency. By comparing project performance against industry standards and best practices, teams can identify opportunities for streamlining processes, reducing waste, and optimizing resource allocation. This, in turn, leads to improved project outcomes and enhanced overall efficiency.

Researching and applying benchmarks effectively

Effectively researching and applying benchmarks is essential for deriving maximum value from benchmarking efforts. Teams should carefully select benchmarks relevant to their project goals and objectives. Additionally, they should develop a systematic approach for collecting, analyzing, and applying benchmark data to inform decision-making and drive project success.

How does Typo help in healthy benchmarking?

Typo is an intelligent engineering platform that finds real-time bottlenecks in your SDLC, automates code reviews, and measures developer experience. It helps engineering leaders compare the team’s results with healthy benchmarks across industries and drive impactful initiatives. This ensures the most accurate, relevant, and comprehensive benchmarks for the entire customer base.

Cycle time benchmarks

Average time all merged pull requests have spent in the “Coding”, “Pickup”, “Review” and “Merge” stages of the pipeline.

Deployment PRs benchmarks

The average number of deployments per week.

Change failure rate benchmarks

The percentage of deployments that fail in production.

Mean time to restore benchmarks

Mean Time to Restore (MTTR) represents the average time taken to resolve a production failure/incident and restore normal system functionality each week.

 

Elite

Good

Fair 

Needs focus

Coding time

Less than 12 hours

12 – 24 hours

24 – 38 hours

More than 38 hours

Pickup time

Less than 7 hours

7 – 12 hours

12 – 18 hours

More than 18 hours

Review time

Less than 6 hours

6 – 13 hours

13 – 28 hours

More than 28 hours

Merge frequency

More than 90% of the PRs merged

80% – 90% of the PRs merged

60% – 80% of the PRs merged

Less than 60% PRs merged

Cycle time

Less than 48 hours

48-94 hours

94-180 hours 

More than 180 hours

Deployment frequency

Daily

More than once/week

Once per week

Less than once/week

Change failure rate

0-15%

15%-30%

30%-50%

More than 50%

MTTR

Less than 1 hour

1-12 hours

12-24 hours

More than 24 hours

PR size

Less than 250 lines of code

250 – 400 lines of code

400 – 600 lines of code

More than 600 lines of code

Rework rate

< 2%

2% – 5%

5% – 7%

> 7%

Refactor rate

< 9%

9% – 15%

15% – 21%

> 21%

Planning accuracy 

More than 90% tasks completed

70%-90% hours

60%-70% hours

Less than 60% hours

If you want to learn more about Typo benchmarks, check out our website now!

Charting a course for success

Engineering benchmarks are invaluable tools for strengthening strategic assumptions and driving project success. By leveraging benchmark data, teams can identify areas for improvement, set realistic goals, and make informed decisions. Engineering teams can enhance efficiency, mitigate risks, and achieve better outcomes by integrating benchmarking practices into their project workflows. With engineering benchmarks as their guide, the path to success becomes clearer and the journey more rewarding.

What is Development Velocity and Why does it Matter?

Software development culture demands speed and quality. To enhance them and drive business growth, it’s essential to cultivate an environment conducive to innovation and streamline the development process.

One such key factor is development velocity which helps in unlocking optimal performance.

Let’s understand more about this term and why it is important:

What is Development Velocity?

Development velocity refers to the amount of work the developers can complete in a specific timeframe. It is the measurement of the rate at which they can deliver business value. In scrum or agile, it is the average number of story points delivered per sprint.

Development velocity is mainly used as a planning tool that helps developers understand how effective they are in deploying high-quality software to end-users.

Why does it Matter?

Development velocity is a strong indicator of whether a business is headed in the right direction. There are various reasons why development velocity is important:

Utilization of Money and Resources

High development velocity leads to an increase in productivity and reduced development time. It further leads to a faster delivery process and reduced time to market which helps in saving cost. Hence, allowing them to maximize the value generated from resources and allocate it to other aspects of business.

Faster Time to Market

High development velocity results in quick delivery of features and updates. Hence, gives the company a competitive edge in the market, responding rapidly to market demands and capturing market opportunities.

Continuous Improvement

Development velocity provides valuable insights into team performance and identifies areas for improvement within the development process. It allows them to analyze velocity trends and implement strategies to optimize their workflow.

Set Realistic Expectations

Development velocity helps in setting realistic expectations by offering a reliable measure of the team’s capacity to deliver work within the timeframe. It further keeps the expectations grounded in reality and fostering trust and transparency within the development team.

Factors that Negatively Impact Development Velocity

A few common hurdles that may impact the developer’s velocity are:

  • High levels of stress and burnout among team members
  • A codebase that lacks CI/CD pipelines
  • Poor code quality or outdated technology
  • Context switching between feature development and operational tasks
  • Accumulated tech debt such as outdated or poorly designed code
  • Manual, repetitive tasks such as manual testing, deployment, and code review processes
  • A complicated organizational structure that challenges coordination and collaboration among team members
  • Developer turnover i.e. attrition or churn
  • Constant distractions that prevent developers from deep, innovative work

How to Measure Development Velocity?

Measuring development velocity includes quantifying the rate at which developers are delivering value to the project.

Although, various metrics measure development velocity, we have curated a few important metrics. Take a look below:

Cycle Time

Cycle Time calculates the time it takes for a task or user story to move from the beginning of the coding task to when it’s been delivered, deployed to production, and made available to users. It provides a granular view of the development process and helps the team identify blindspots and ways to improve them.

Story Points

Story points measure the number of story points completed over a period of time, typically within a sprint. Tracking the total story points in each iteration or sprint estimates future performance and resource allocation.

User Stories

User stories measure the velocity in terms of completed user stories. It gives a clear indication of progress and helps in planning future iterations. Moreover, measuring user stories helps in planning and prioritizing their work efforts while maintaining a sustainable pace of delivery.

Burndown Chart

The Burndown chart tracks the remaining work in a sprint or iteration. Comparing planned work against the actual work progress helps in assessing their velocity and comparing progress to sprint goals. This further helps them in making informed decisions to identify velocity trends and optimize their development process.

What Is A Burndown Chart: Meaning & How To Use It – Forbes Advisor INDIA

Engineering Hours

Engineering hours track the actual time spent by engineers on specific tasks or user stories. It is a direct measure of effort and helps in estimating future tasks based on historical data. It provides feedback for continuous improvement efforts and enables them to make data-driven decisions and improve performance.

Lead Time

Lead time calculates the time between committing the code and sending it to production. However, it is not a direct metric and it needs to complement other metrics such as cycle time and throughput. It helps in understanding how quickly the development team is able to respond to new work and deliver value.

How to Improve Development Velocity?

Build a Positive Developer Experience

Developers are important assets of software development companies. When they are unhappy, this leads to reduced productivity and morale. This further lowers code quality and creates hurdles in collaboration and teamwork. As a result, this negatively affects the development velocity.

Hence, the first and most crucial way is to create a positive work environment for developers. Below are a few ways how you can build a positive developer experience for them:

Foster a Culture of Experimentation

Encouraging a culture of experimentation and continuous learning leads to innovation and the adoption of more efficient practices. Let your developers, experiment, make mistakes and try again. Ensure that you acknowledge their efforts and celebrate their successes.

Set Realistic Deadlines

Unrealistic deadlines can cause burnout, poor code quality work, and negligence in PR review. Always involve your development team while setting deadlines. When set right, it can help them plan and prioritize their tasks. Ensure that you give buffer time to them to manage roadblocks and unexpected bugs as well as other priorities.

Encourage Frequent Communication and Two-Way Feedback

Regular communication among team leaders and developers lets them share important information on a priority basis. It allows them to effectively get their work done since they are communicating their progress and blockers while simultaneously moving on with their tasks.

Encourage Pair Programming

Knowledge sharing and collaboration are important. This can be through pair programming and collaborating with other developers as it allows them to work on more complex problems and code together in parallel. It also results in effective communication as well as accountability for each other’s work.

Manage Technical Debt

An increase in technical debt negatively impacts the development velocity. When teams take shortcuts, they have to spend extra time and effort on fixing bugs and other issues. It also leads to improper planning and documentation which further slows down the development process.

Below are a few ways how developers can minimize technical debt:

Automated Testing

The automated testing process minimizes the risk of errors in the future and identifies defects in code quickly. Further, it increases the efficiency of engineers. Hence, giving them more time to solve problems that need human interference.

Regular Code Reviews

Code reviews in routine allow the team to handle technical debt in the long run. As it helps in constant error checking and catching potential issues which enhance code quality.

Refactoring

Refactoring involves making changes to the codebase without altering its external behavior. It is an ongoing process that is performed regularly throughout the software development life cycle.

Listen to your Engineers

Always listen to your engineers. They are the ones who are well aware of ongoing development and working closely with a database and developing the applications. Listen to what they have to say and take their suggestions and opinions.

Adhere to Agile Methodologies

Agile methodologies such as Scrum and Kanban offer a framework to manage software development projects flexibly and seamlessly. This is because the framework breaks down projects into smaller, manageable increments. Hence, allowing them to focus on delivering small pieces of functions more quickly. It also enables developers to receive feedback quickly and have constant communication with the team members.

The agile methodology also prioritizes work based on business value, customer needs and dependencies to streamline developers’ efforts and maintain consistent progress.

Align Objectives with Other Teams

One of the best ways the software development process works efficiently is when everyone’s goals are aligned. If not, it could lead to being out of sync and stuck in a bottleneck situation. Aligning objectives with other teams fosters collaboration reduces duplication of efforts, and ensures that everyone is working towards the same goal.

Moreover, it minimizes the conflicts and dependencies between teams enabling faster decision making and problem-solving. Hence, development teams should regularly communicate, coordinate, and align with priorities to ensure a shared understanding of objectives and vision.

Empower Developers with the Right Tools

Right engineering tools and technologies can help in increasing productivity and development velocity. Organizations that have tools for continuous integration and deployment, communication, collaboration, planning, and development are likely more innovative than the companies that don’t use them.

There are many tools available in the market. Below are key factors that the engineering team should keep in mind while choosing any engineering tool:

  • Understand the specific requirements and workflows of your development team.
  • Evaluate the features and capabilities of each tool to determine if they meet your team’s needs.
  • Consider the cost of implementing and maintaining the tools, including licensing fees, subscription costs, training expenses, and ongoing support.
  • Ensure that the selected tools are compatible with your existing technology stack and can seamlessly integrate with other tools and systems.
  • Continuously gather feedback from users, monitor performance metrics, and be willing to iterate and make adjustments as needed to ensure that your team has the right tools to support their development efforts effectively.

Enhance Development Velocity with Typo

As mentioned above, empowering your development team to use the right tools is crucial. Typo is one such intelligent engineering platform that is used for gaining visibility, removing blockers, and maximizing developer effectiveness.

  • Typo’s automated code review tool auto-analyses codebase and pull requests to find issues and auto-generates fixes before it merges to master. It understands the context of your code and quickly finds and fixes any issues accurately, making pull requests easy and stress-free.
  • Its effective sprint analysis feature tracks and analyzes the team’s progress throughout a sprint. It uses data from Git and the issue management tool to provide insights into getting insights on how much work has been completed, how much work is still in progress, and how much time is left in the sprint.
  • Typo has a metrics dashboard that focuses on the team’s health and performance. It lets engineering leaders compare the team’s results with what healthy benchmarks across industries look like and drive impactful initiatives for your team.
  • This platform helps in getting a 360 view of the developer experience as it captures qualitative insights and provides an in-depth view of the real issues that need attention. With signals from work patterns and continuous AI-driven pulse check-ins on the experience of developers in the team, Typo helps with early indicators of their well-being and actionable insights on the areas that need your attention.
  • The more the tools can be integrated with software, the better it is for the software developers. Typo lets you see the complete picture of your engineering health by seamlessly connecting to your tech tool stack such as GIT versioning, issue tracker, and CI/CD tools.

Best DORA Metrics Trackers for 2024

DevOps is a set of practices that promotes collaboration and communication between software development and IT operations teams. It has become a crucial part of the modern software development landscape.

Within DevOps, DORA metrics (DevOps Research and Assessment) are essential in evaluating and improving performance. This guide is aimed at providing a comprehensive overview of the best DORA metrics trackers for 2024. It offers insights into their features and benefits to help organizations optimize their DevOps practices.

What are DORA Metrics?

DORA metrics serve as a compass for evaluating software development performance. Four key metrics include deployment frequency, change lead time, change failure rate, and mean time to recovery (MTTR).

Deployment Frequency

Deployment frequency measures how often code is deployed to production.

Change Lead Time

It is essential to measure the time taken from code creation to deployment, known as change lead time. This metric helps to evaluate the efficiency of the development pipeline.

Change Failure Rate

Change failure rate measures a team’s ability to deliver reliable code. By analyzing the rate of unsuccessful changes, teams can identify areas for improvement in their development and deployment processes.

Mean time to recovery (MTTR)

Mean Time to Recovery (MTTR) is a metric that measures the amount of time it takes a team to recover from failures.

Best DORA Metrics Tracker

Typo

Typo establishes itself as a frontrunner among DORA metrics trackers. It is an intelligent engineering management platform used for gaining visibility, removing blockers, and maximizing developer effectiveness. Typo’s user-friendly interface and cutting-edge capabilities set it apart in the competitive landscape.

Key Features

  • Customizable DORA metrics dashboard: Users can tailor the DORA metrics dashboard to their specific needs, providing a personalized and efficient monitoring experience. It provides a user-friendly interface and integrates with DevOps tools to ensure a smooth data flow for accurate metric representation.
  • Code review automation: Typo is an automated code review tool that not only enables developers to catch issues related to code maintainability, readability, and potential bugs but also can detect code smells. It identifies issues in the code and auto-fixes them before you merge to master.
  • Predictive sprint analysis: Typo’s intelligent algorithm provides you with complete visibility of your software delivery performance and proactively tells which sprint tasks are blocked, or are at risk of delay by analyzing all activities associated with the task.
  • Measures developer experience: While DORA metrics provide valuable insights, they alone cannot fully address software delivery and team performance. With Typo’s research-backed framework, gain qualitative insights across developer productivity and experience to know what’s causing friction and how to improve.
  • High number of integrations: Typo seamlessly integrates with the tech tool stack. It includes GIT versioning, Issue tracker, CI/CD, communication, Incident management, and observability tools.

Comparative Advantage

In direct comparison to alternative trackers, Typo distinguishes itself through its intuitive design and robust functionality for engineering teams. While other options may excel in certain aspects, Typo strikes a balance by delivering a holistic solution that caters to a broad spectrum of DevOps requirements.

Typo’s prominence in the field is underscored by its technical capabilities and commitment to providing a user-centric experience. This blend of innovation, adaptability, and user-friendliness positions Typo as the leading choice for organizations seeking to elevate their DORA metrics tracking in 2024.

LinearB

LinearB introduces a collaborative approach to DORA metrics, emphasizing features that enhance teamwork and overall efficiency. Real-world examples demonstrate how collaboration can significantly impact DevOps performance, making LinearB a standout choice for organizations prioritizing team synergy and collaboration.

platform/dora/dora hero

Key Features

  • Shared metrics visibility: LinearB promotes shared metrics visibility, ensuring that the software team has a transparent view of key DORA metrics. This fosters a collaborative environment where everyone is aligned toward common goals.
  • Real-time collaboration: The ability to collaborate in real-time is a crucial feature of LinearB. Teams can respond promptly to changing circumstances, fostering agility and responsiveness in their DevOps processes.
  • Integrations with popular tools: LinearB integrates seamlessly with popular development tools, enhancing collaboration by bringing metrics directly into the tools that teams already use.

LinearB’s focus on collaboration shared visibility, and real-time interactions positions it as a tool that tracks metrics and actively contributes to improved team dynamics and overall DevOps performance.

Jellyfish

Jellyfish excels in adapting to diverse DevOps environments, offering customizable options and seamless integration capabilities. Whether deployed in the cloud or on-premise setups, Jellyfish ensures a smooth and adaptable tracking experience for DevOps teams seeking flexibility in their metrics monitoring.

Key Features

  • Customization options: Jellyfish provides extensive customization options, allowing organizations to tailor the tool to their specific needs and preferences. This adaptability ensures that Jellyfish can seamlessly integrate into existing workflows.
  • Seamless integration: The ability of Jellyfish to integrate seamlessly with various DevOps tools, both in the cloud and on-premise, makes it a versatile choice for organizations with diverse technology stacks.
  • Flexibility in deployment: Whether organizations operate primarily in cloud environments, on-premise setups, or a hybrid model, Jellyfish is designed to accommodate different deployment scenarios, ensuring a smooth tracking experience in any context.

Jellyfish’s success is further showcased through real-world implementations, highlighting its flexibility and ability to meet the unique requirements of different DevOps environments. Its adaptability positions Jellyfish as a reliable and versatile choice for organizations navigating the complexities of modern software development.

Faros

Faros stands out as a robust DORA metrics tracker, emphasizing precision and effectiveness in measurement. Its feature set is specifically designed to ensure the accurate evaluation of critical metrics such as deployment frequency, lead time for changes, change failure rate, and mean time to recover. Faros’ impact extends to industries with stringent requirements, notably finance, and healthcare, where precise metrics are imperative for success.

Key Features

  • Accurate measurement: Faros’ core strength lies in its ability to provide accurate and reliable measurements of key DORA metrics. This precision is crucial for organizations that make data-driven decisions and optimize their DevOps processes.
  • Industry-specific solutions: Tailored solutions for finance and healthcare industries demonstrate Faros’ versatility in catering to the unique needs of different sectors. These specialized features make it a preferred choice for organizations with specific compliance and regulatory requirements.

Faros, focusing on precision and industry-specific solutions, positions itself as an indispensable tool for organizations that prioritize accuracy and reliability in their DORA metrics tracking.

Haystack

Haystack simplifies the complexity associated with DORA metrics tracking through its user-friendly features. The efficiency of Haystack is evident in its customizable dashboards and streamlined workflows, offering a solution tailored for teams seeking simplicity and efficiency in their DevOps practices.

Key Features

  • User-Friendly interface: Haystack’s user interface is designed with simplicity in mind, making it accessible to users with varying levels of technical expertise. This ease of use promotes widespread adoption within diverse teams.
  • Customizable dashboards: The ability to customize dashboards allows teams to tailor the tracking experience to their specific requirements, fostering a more personalized and efficient approach.
  • Streamlined workflows: Haystack’s emphasis on streamlined workflows ensures that teams can navigate the complexities of DORA metrics tracking with ease, reducing the learning curve associated with new tools.

Success stories further underscore the positive impact Haystack has on organizations navigating complex DevOps landscapes. The combination of user-friendly features and efficient workflows positions Haystack as an excellent choice for teams seeking a straightforward yet powerful DORA metrics tracking solution.

Typo vs. Competitors

Choosing the right tool can be overwhelming so here are some factors for you to consider Typo as the leading choice:

Code Review Workflow Automation

Typo’s automated code review tool not only enables developers to catch issues related to code maintainability, readability, and potential bugs but also can detect code smells. It identifies issues in the code and auto-fixes them before you merge to master.

Focuses on Developer Experience

In comparison to other trackers, Typo offers a 360 view of your developer experience. It helps in identifying the key priority areas affecting developer productivity and well-being as well as benchmark performance by comparing results against relevant industries and team sizes.

Customer Support

Typo’s commitment to staying ahead in the rapidly evolving DevOps space is evident through its customer support as the majority of the end-users’ queries are solved within 24-48 hours.

Choose the Best DORA Metrics Tracker for your Business

If you’re looking for a DORA metrics tracker that can help you optimize DevOps performance, Typo is the ideal solution for you. With its unparalleled features, intuitive design, and ongoing commitment to innovation, Typo is the perfect choice for software development teams seeking a solution that seamlessly integrates with their CI/CD pipelines, offers customizable dashboards, and provides real-time insights.

Typo not only addresses common pain points but also offers a comprehensive solution that can help you achieve your organizational goals. It’s easy to get started with Typo, and we’ll guide you through the process step-by-step to ensure that you can harness its full potential for your organization’s success.

So, if you’re ready to take your DevOps performance to the next level..

DORA Metrics Explained: Your Comprehensive Resource

In the constantly changing world of software development, it is crucial to have reliable metrics to measure performance. This guide provides a detailed overview of DORA (DevOps Research and Assessment) metrics, explaining their importance in assessing the effectiveness, efficiency, and dependability of software development processes.

What are DORA Metrics?

DORA metrics serve as a compass for evaluating software development performance. This guide covers deployment frequency, change lead time, change failure rate, and mean time to recovery (MTTR).

The Four Key DORA Metrics

Let’s explore the key DORA metrics that are crucial for assessing the efficiency and reliability of software development practices. These metrics provide valuable insights into a team's agility, adaptability, and resilience to change.

Deployment Frequency

Deployment Frequency measures how often code is deployed to production. The frequency of code deployment reflects how agile, adaptable, and efficient the team is in delivering software solutions. This metric, explained in our guide, provides valuable insights into the team's ability to respond to changes, enabling strategic adjustments in development practices.

Change Lead Time

It is essential to measure the time taken from code creation to deployment, which is known as change lead time. This metric helps to evaluate the efficiency of the development pipeline, emphasizing the importance of quick transitions from code creation to deployment. Our guide provides a detailed analysis of how optimizing change lead time can significantly improve overall development practices.

Change Failure Rate

Change failure rate measures a team's ability to deliver reliable code. By analyzing the rate of unsuccessful changes, teams can identify areas for improvement in their development and deployment processes. This guide provides detailed insights on interpreting and leveraging change failure rate to enhance code quality and reliability.

Mean Time to Recovery (MTTR)

Mean Time to Recovery (MTTR) is a metric that measures the amount of time it takes a team to recover from failures. This metric is important because it helps gauge a team's resilience and recovery capabilities, which are crucial for maintaining a stable and reliable software environment. Our guide will explore how understanding and optimizing MTTR can contribute to a more efficient and resilient development process.

Below are the performance metrics categorized in

  • Elite performers
  • High performers
  • Medium performers
  • Low performers

for 4 metrics –

Use Four Keys metrics like change failure rate to measure your DevOps  performance | Google Cloud Blog

Utilizing DORA Metrics for DevOps Teams

Utilizing DORA (DevOps Research and Assessment) metrics goes beyond just understanding individual metrics. It involves delving into the practical application of DORA metrics that are specifically tailored for DevOps teams. By actively tracking and reporting on these metrics over time, teams can gain actionable insights, identify trends, and patterns, and pinpoint areas for continuous improvement. Furthermore, by aligning DORA metrics with business value, organizations can ensure that their DevOps efforts contribute directly to strategic objectives and overall success.

Establishing a Baseline

The guide recommends that engineering teams begin by assessing their current DORA metric values to establish a baseline. This baseline is a reference point for measuring progress and identifying deviations over time. By understanding their deployment frequency, change lead time, change failure rate, and MTTR, teams can set realistic improvement goals specific to their needs.

Identifying Trends and Patterns

Consistently monitoring DORA (DevOps Research and Assessment) metrics helps software teams detect patterns and trends in their development and deployment processes. This guide provides valuable insights into how analyzing deployment frequency trends can reveal the team's ability to adapt to changing requirements while assessing change lead time trends can offer a glimpse into the workflow's efficiency. By identifying patterns in change failure rates, teams can pinpoint areas that need improvement, enhancing the overall software quality and reliability.

Continuous Improvement Strategies

Using DORA metrics is a way for DevOps teams to commit to continuously improving their processes and track progress. The guide promotes an iterative approach, encouraging teams to use metrics to develop targeted strategies for improvement. By optimizing deployment pipelines, streamlining workflows, or improving recovery mechanisms, DORA metrics can help drive positive changes in the development lifecycle.

Cross-Functional Collaboration

The DORA metrics have practical implications in promoting cross-functional cooperation among DevOps teams. By jointly monitoring and analyzing metrics, teams can eliminate silos and strive towards common goals. This collaborative approach improves communication, speeds up decision-making, and ensures that everyone is working towards achieving shared objectives.

Feedback-Driven Development

DORA metrics form the basis for establishing a culture of feedback-driven development within DevOps teams. By consistently monitoring metrics and analyzing performance data, teams can receive timely feedback, allowing them to quickly adjust to changing circumstances. This ongoing feedback loop fosters a dynamic development environment where real-time insights guide continuous improvements. Additionally, aligning DORA metrics with operational performance metrics enhances the overall understanding of system behavior, promoting more effective decision-making and streamlined operational processes.

Practical Application of DORA Metrics

DORA metrics isn’t just a mere theory to support DevOps but it has practical applications to elevate how your team works. Here are some of them:

Measuring Speed

Efficiency and speed are crucial in software development. The guide explores methods to measure deployment frequency, which reveals how frequently code is deployed to production. This measurement demonstrates the team's agility and ability to adapt quickly to changing requirements. This emphasizes a culture of continuous delivery.

Ensuring Quality

Quality assurance plays a crucial role in software development, and the guide explains how DORA metrics help in evaluating and ensuring code quality. By analyzing the change failure rate, teams can determine the dependability of their code modifications. This helps them recognize areas that need improvement, promoting a culture of delivering top-notch software.

Ensuring Reliability

Reliability is crucial for the success of software applications. This guide provides insights into Mean Time to Recovery (MTTR), a key metric for measuring a team's resilience and recovery capabilities. Understanding and optimizing MTTR contributes to a more reliable development process by ensuring prompt responses to failures and minimizing downtime.

Benchmarking for Improvement

Benchmarks play a crucial role in measuring the performance of a team. By comparing their performance against both the industry standards and their own team-specific goals, software development teams can identify areas that need improvement. This iterative process allows for continuous execution enhancement, which aligns with the principles of continuous improvement in DevOps practices.

Value Stream Management

Value Stream Management is a crucial application of DORA metrics. It provides development teams with insights into their software delivery processes and helps them optimize for efficiency and business value. It enables quick decision-making, rapid response to issues, and the ability to adapt to changing requirements or market conditions.

Challenges of Implementing DORA Metrics

Implementing DORA metrics brings about a transformative shift in the software development process, but it is not without its challenges. Let’s explore the potential hurdles faced by teams adopting DORA metrics and provide insightful solutions to navigate these challenges effectively.

Resistance to Change

One of the main challenges faced is the reluctance of the development team to change. The guide explores ways to overcome this resistance, emphasizing the importance of clear communication and highlighting the long-term advantages that DORA metrics bring to the development process. By encouraging a culture of flexibility, teams can effectively shift to a DORA-centric approach.

Lack of Data Visibility

To effectively implement DORA metrics, it is important to have a clear view of data across the development pipeline. The guide provides solutions for overcoming challenges related to data visibility, such as the use of integrated tools and platforms that offer real-time insights into deployment frequency, change lead time, change failure rate, and MTTR. This ensures that teams are equipped with the necessary information to make informed decisions.

Overcoming Silos

Organizational silos can hinder the smooth integration of DORA metrics into the software development workflow. In this guide, we explore different strategies that can be used to break down these silos and promote cross-functional collaboration. By aligning the goals of different teams and working together towards a unified approach, organizations can fully leverage the benefits of DORA metrics in improving software development performance.

Ensuring Metric Relevance

Ensuring the success of DORA implementation relies heavily on selecting and defining relevant metrics. The guide emphasizes the importance of aligning the chosen metrics with organizational goals and objectives to overcome the challenge of ensuring metric relevance. By tailoring metrics to specific needs, teams can extract meaningful insights for continuous improvement.

Scaling Implementation

Implementing DORA metrics across multiple teams and projects can be a challenge for larger organizations. To address this challenge, the guide offers strategies for scaling the implementation. These strategies include the adoption of standardized processes, automated tools, and consistent communication channels. By doing so, organizations can achieve a harmonized approach to DORA metrics implementation.

Future Trends in DORA Metrics

Anticipating future trends in DORA metrics is essential for staying ahead in the dynamic landscape of software development. Here are some of them:

Integration with AI and Machine Learning

As the software development landscape continues to evolve, there is a growing trend towards integrating DORA metrics with artificial intelligence (AI) and machine learning (ML) technologies. These technologies can enhance predictive analytics, enabling teams to proactively identify potential bottlenecks, optimize workflows, and predict failure rates. This integration empowers organizations to make data-driven decisions, ultimately improving the overall efficiency and reliability of the development process.

Expansion of Metric Coverage

DORA metrics are expected to expand their coverage beyond the traditional four key metrics. This expansion may include metrics related to security, collaboration, and user experience, allowing teams to holistically assess the impact of their development practices on various aspects of software delivery.

Continuous Feedback and Iterative Improvement

Future trends in DORA metrics emphasize the importance of continuous feedback loops and iterative improvement. Organizations are increasingly adopting a feedback-driven culture, leveraging DORA metrics to provide timely insights into the development process. This iterative approach enables teams to identify areas for improvement, implement changes, and measure the impact, fostering a cycle of continuous enhancement.

Enhanced Visualization and Reporting

Advancements in data visualization and reporting tools are shaping the future of DORA metrics. Organizations are investing in enhanced visualization techniques to make complex metric data more accessible and actionable. Improved reporting capabilities enable teams to communicate performance insights effectively, facilitating informed decision-making at all levels of the organization.

DORA Metrics is crucial for your organization

DORA metrics in software development serve as both evaluative tools and innovators, playing a crucial role in enhancing Developer Productivity and guiding engineering leaders. DevOps practices rely on deployment frequency, change lead time, change failure rate, and MTTR insights gained from DORA metrics. They create a culture of improvement, collaboration, and feedback-driven development. Future integration with AI, expanded metric coverage, and enhanced visualization herald a shift in navigating the complex landscape. Metrics have transformative power in guiding DevOps teams towards resilience, efficiency, and success in a constantly evolving technological landscape.

What is the Mean Time to Recover (MTTR) in DORA Metrics?

The Mean Time to Recover (MTTR) is a crucial measurement within DORA (DevOps Research and Assessment) metrics. It provides insights into how fast an organization can recover from disruptions. In this blog post, we will discuss the importance of MTTR in DevOps and its role in improving system reliability while reducing downtime.

What is the Mean Time to Recover (MTTR)?

MTTR, which stands for Mean Time to Recover, is a valuable metric that calculates the average duration taken by a system or application to recover from a failure or incident. It is an essential component of the DORA metrics and concentrates on determining the efficiency and effectiveness of an organization's incident response and resolution procedures.

Importance of MTTR

It is a useful metric to measure for various reasons:

  • Minimizing MTTR enhances user satisfaction by reducing downtime and resolution times.
  • Reducing MTTR mitigates the negative impacts of downtime on business operations, including financial losses, missed opportunities, and reputational damage.
  • Helps meet service level agreements (SLAs) that are vital for upholding client trust and fulfilling contractual commitments.

Essence of Mean Time to Recover in DevOps

Efficient incident resolution is crucial for maintaining seamless operations and meeting user expectations. MTTR plays a pivotal role in the following aspects:

Rapid Incident Response

MTTR is directly related to an organization's ability to respond quickly to incidents. A lower MTTR indicates a DevOps team that is more agile and responsive and can promptly address issues.

Minimizing Downtime

Organizations' key goal is to minimize downtime. MTTR quantifies the time it takes to restore normalcy, reducing the impact on users and businesses. software delivery software development software development

Enhancing User Experience

A fast recovery time leads to a better user experience. Users appreciate services that have minimal disruptions, and a low MTTR shows a commitment to user satisfaction.

Calculating Mean Time to Recover (MTTR)

It is a key metric that encourages DevOps teams to build more robust systems. Besides this, it is completely different from the other three DORA metrics.

MTTR metric measures the severity of the impact. It indicates how quickly DevOps can acknowledge unplanned breakdowns and repair them, providing valuable insights into incident response time.

To calculate this, add up the total downtime and divide it by the total number of incidents that occurred within a particular period. For example, the time spent on unplanned maintenance is 60 hours. The total number of incidents that occurred is 10 times. Hence, the mean time to recover would be 6 hours.

 

Mean time to recover

Elite performers

Less than 1 hour

High performers

Less than 1 day

Medium performers

1 day to 1 week

Low performers

1 month to 6 months

The response time should be as short as possible. 24 hours is considered to be a good rule of thumb.

High Mttr means the product will be unavailable to end users for a longer time period. This further results in lost revenue, productivity, and customer dissatisfaction. DevOps needs to ensure continuous monitoring and prioritize recovery when a failure occurs.

With Typo, you can improve dev efficiency with an inbuilt DORA metrics dashboard.

  • With pre-built integrations in your dev tool stack, get all the relevant data flowing in within minutes and see it configured as per your processes.
  • Gain visibility beyond DORA by diving deep and correlating different metrics to identify real-time bottlenecks, sprint delays, blocked PRs, deployment efficiency, and much more from a single dashboard.
  • Set custom improvement goals for each team and track their success in real time. Also, stay updated with nudges and alerts in Slack.

Use Cases

Downtime can be detrimental, impacting revenue and customer trust. MTTR measures the time taken to recover from a failure. A high MTTR indicates inefficiencies in issue identification and resolution. Investing in automation, refining monitoring systems, and bolstering incident response protocols minimizes downtime, ensuring uninterrupted services.

Quality Deployments

Metrics: Change Failure Rate and Mean Time to Recovery (MTTR)

Low Change Failure Rate, Swift MTTR

Low deployment failures and a short recovery time exemplify quality deployments and efficient incident response. Robust testing and a prepared incident response strategy minimize downtime, ensuring high-quality releases and exceptional user experiences.

High Change Failure Rate, Rapid MTTR

A high failure rate alongside swift recovery signifies a team adept at identifying and rectifying deployment issues promptly. Rapid responses minimize impact, allowing quick recovery and valuable learning from failures, strengthening the team’s resilience.

Mean Time to Recover and its Importance with Organization Performance

MTTR is more than just a metric; it reflects engineering teams' commitment to resilience, customer satisfaction, and continuous improvement. A low MTTR signifies:

Robust Incident Management

Having an efficient incident response process indicates a well-structured incident management system capable of handling diverse challenges.

Proactive Problem Solving

Proactively identifying and addressing underlying issues can prevent recurrent incidents and result in low MTTR values.

Building Trust

Trust plays a crucial role in service-oriented industries. A low mean time to resolve (MTTR) builds trust among users, stakeholders, and customers by showcasing reliability and a commitment to service quality.

Operational Efficiency

Efficient incident recovery ensures prompt resolution without workflow disruption, leading to operational efficiency.

User Satisfaction

User satisfaction is directly proportional to the reliability of the system. A low Mean Time To Repair (MTTR) results in a positive user experience, which enhances overall satisfaction.

Business Continuity

Minimizing downtime is crucial to maintain business continuity and ensure critical systems are consistently available.

Strategies for Improving Mean Time to Recover (MTTR)

Optimizing MTTR involves implementing strategic practices to enhance incident response and recovery. Key strategies include:

Automation

Leveraging automation for incident detection, diagnosis, and recovery can significantly reduce manual intervention, accelerating recovery times.

Collaborative Practices

Fostering collaboration among development, operations, and support teams ensures a unified response to incidents, improving overall efficiency.

Continuous Monitoring

Implement continuous monitoring for real-time issue detection and resolution. Monitoring tools provide insights into system health, enabling proactive incident management.

Training and Skill Development

Investing in team members' training and skill development can improve incident efficiency and reduce MTTR.

Incident Response Team

Establishing a dedicated incident response team with defined roles and responsibilities contributes to effective incident resolution. This further enhances overall incident response capabilities.

Building Resilience with MTTR in DevOps

The Mean Time to Recover (MTTR) is a crucial measure in the DORA framework that reflects engineering teams' ability to bounce back from incidents, work efficiently, and provide dependable services. To improve incident response times, minimize downtime, and contribute to their overall success, organizations should recognize the importance of MTTR, implement strategic improvements, and foster a culture of continuous enhancement. Key Performance Indicator considerations play a pivotal role in this process.

For teams seeking to stay ahead in terms of productivity and workflow efficiency, Typo offers a compelling solution. Uncover the complete spectrum of Typo's capabilities designed to enhance your team's productivity and streamline workflows. Whether you're aiming to optimize work processes or foster better collaboration, Typo's impactful features, aligned with Key Performance Indicator objectives, provide the tools you need. Embrace heightened productivity by unlocking the full potential of Typo for your team's success today.

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How to Measure DORA Metrics?

DevOps Research and Assessment (DORA) metrics are a compass for engineering teams striving to optimize their development and operations processes. This detailed guide will explore each facet of measuring DORA metrics to empower your journey toward DevOps excellence.

Understanding the Four Key DORA Metrics

Given below are four key DORA metrics that help in measuring software delivery performance:

Deployment Frequency

Deployment frequency is a key indicator of agility and efficiency. Regular deployments signify a streamlined pipeline, allowing teams to deliver features and updates faster.It is important to measure Deployment Frequency for various reasons:

  • It provides insights into the overall efficiency and speed of the development team’s processes. Besides this, Deployment Frequency also highlights the stability and reliability of the production environment. 
  • It helps in identifying pitfalls and areas for improvement in the software development life cycle. 
  • It helps in making data-driven decisions to optimize the process. 
  • It helps in understanding the impact of changes on system performance. 

Lead Time for Changes

This metric measures the time it takes for code changes to move from inception to deployment. A shorter lead time indicates a responsive development cycle and a more efficient workflow.It is important to measure Lead Time for Changes for various reasons:

  • Short lead times in software development are crucial for success in today’s business environment. By delivering changes rapidly, organizations can seize new opportunities, stay ahead of competitors, and generate more revenue.
  • Short lead time metrics help organizations gather feedback and validate assumptions quickly, leading to informed decision-making and aligning software development with customer needs. Being customer-centric is critical for success in today’s competitive world, and feedback loops play a vital role in achieving this.
  • By reducing lead time, organizations gain agility and adaptability, allowing them to swiftly respond to market changes, embrace new technologies, and meet evolving business needs. Shorter lead times enable experimentation, learning, and continuous improvement, empowering organizations to stay competitive in dynamic environments.
  • Reducing lead time demands collaborative teamwork, breaking silos, fostering shared ownership, and improving communication, coordination, and efficiency. 

Mean Time to Recovery

The mean time to recovery reflects how quickly a team can bounce back from incidents or failures. A lower mean time to recovery is synonymous with a resilient system capable of handling challenges effectively.

It is important to Mean Time to Recovery for various reasons:

  • Minimizing MTTR enhances user satisfaction by reducing downtime and resolution times.
  • Reducing MTTR mitigates the negative impacts of downtime on business operations, including financial losses, missed opportunities, and reputational damage.
  • Helps meet service level agreements (SLAs) that are vital for upholding client trust and fulfilling contractual commitments.

Change Failure Rate

Change failure rate gauges the percentage of changes that fail. A lower failure rate indicates a stable and reliable application, minimizing disruptions caused by failed changes.

Understanding the nuanced significance of each metric is essential for making informed decisions about the efficacy of your DevOps processes.

It is important to measure the Change Failure Rate for various reasons:

  • A lower change failure rate enhances user experience and builds trust by reducing failures; we elevate satisfaction and cultivate lasting positive relationships.
  • It protects your business from financial risks, and you avoid revenue loss, customer churn, and brand damage by reducing failures.
  • Reduce change failures to allocate resources effectively and focus on delivering new features.

Utilizing Specialized Tools for Precision Measurement

Efficient measurement of DORA metrics, crucial for optimizing deployment processes and ensuring the success of your DevOps team, requires the right tools, and one such tool that stands out is Typo.

Why Typo?

Typo is a powerful tool designed specifically for tracking and analyzing DORA metrics, providing an alternative and efficient solution for development teams seeking precision in their DevOps performance measurement.

Steps to Measure DORA Metrics with Typo

Typo is a software delivery management platform used for gaining visibility, removing blockers, and maximizing developer effectiveness. Typo integrates with your tech stacks like Git providers, issue trackers, CI/CD, and incident tools to identify key blockers in the dev processes and stay aligned with business goals.

Step 1

Visit our website https://typoapp.io/dora-metrics and sign up using your preferred version control system (Github, Gitlab, or Bitbucket).

Step 2

Follow the onboarding process detailed on the website and connect your git, issue tracker, and Slack.

Step 3

Based on the number of members and repositories, Typo automatically syncs with your git and issue tracker data and shows insights within a few minutes.

Step 4

Lastly, set your metrics configuration specific to your development processes as mentioned below:

Deployment Frequency Setup

For setting up Deployment Frequency, you need to provide us with the details of how your team identifies deployments with other details like the name of the branches- Main/Master/Production you use for production deployment.

Screenshot 2024-03-16 at 12.24.04 AM.png

Synchronize CFR & MTTR without Incident Management

If there is a process you follow to detect deployment failures, for example, if you use labels like hotfix, rollbacks, etc for identifying PRs/tasks created to fix failed deployments, Typo will read those labels accordingly and provide insights based on your failure rate and the time to restore from those failures.

Cycle Time

Cycle time is automatically configured when setting up the DORA metrics dashboard. Typo Cycle Time takes into account pull requests that are still in progress. To calculate the Cycle Time for open pull requests, they are assumed to be closed immediately.

Screenshot 2024-03-16 at 1.14.10 AM.png

Advantages of Using Typo:

  • User-Friendly Interface: Typo's intuitive interface makes it accessible to DevOps professionals and decision-makers.
  • Customization: Tailor the tool to suit your organization's specific needs and metrics priorities.
  • Integration Capabilities: Typo integrates with popular Dev tools, ensuring a cohesive measurement experience.
  • Value Stream Management: Typo streamlines your value delivery process, aligning your efforts with business objectives for enhanced organizational performance.
  • Business Value Optimization: Typo assists software teams in gaining deeper insights into your development processes, translating them into tangible business value. 
  • DORA metrics dashboard: The DORA metrics dashboard plays a crucial role in optimizing DevOps performance. It also provides benchmarks to identify where you stand based on your team’s performance.  Building the dashboard with Typo provides various benefits such as tailored integration and customization for software development teams.

Continuous Improvement: A Cyclical Process

In the rapidly changing world of DevOps, attaining excellence is not an ultimate objective but an ongoing and cyclical process. To accomplish this, measuring DORA (DevOps Research and Assessment) metrics becomes a vital aspect of this journey, creating a continuous improvement loop that covers every stage of your DevOps practices.

Understanding the Cyclical Nature

Measuring beyond Number

The process of measuring DORA metrics is not simply a matter of ticking boxes or crunching numbers. It is about comprehending the narrative behind these metrics and what they reveal about your DevOps procedures. The cycle starts by recognizing that each metric represents your team's effectiveness, dependability, and flexibility.

Regular Analysis

Consistency is key to making progress. Establish a routine for reviewing DORA metrics – this could be weekly, monthly, or by your development cycles. Delve into the data, and analyze the trends, patterns, and outliers. Determine what is going well and where there is potential for improvement.

Identifying Areas for Enhancement

During the analysis phase, you can get a comprehensive view of your DevOps performance. This will help you identify the areas where your team is doing well and the areas that need improvement. The purpose of this exercise is not to assign blame but to gain a better understanding of your DevOps ecosystem's dynamics.

Implementing Changes with Purpose

Iterative Adjustments

After gaining insights from analyzing DORA metrics, implementing iterative changes involves fine-tuning the engine rather than making drastic overhauls.

Experimentation and Innovation

Continuous improvement is fostered by a culture of experimentation. It's important to motivate your team to innovate and try out new approaches, such as adjusting deployment frequencies, optimizing lead times, or refining recovery processes. Each experiment contributes to the development of your DevOps practices and helps you evolve and improve over time.

Learning from Failures

Rather than viewing failure as an outcome, see it as an opportunity to gain knowledge. Embrace the mindset of learning from your failures. If a change doesn't produce the desired results, use it as a chance to gather information and enhance your strategies. Your failures can serve as a foundation for creating a stronger DevOps framework.

Optimizing DevOps Performance Continuously

Adaptation to Changing Dynamics

DevOps is a constantly evolving practice that is influenced by various factors like technology advancements, industry trends, and organizational changes. Continuous improvement requires staying up-to-date with these dynamics and adapting DevOps practices accordingly. It is important to be agile in response to change.

Feedback Loops

It's important to create feedback loops within your DevOps team. Regularly seek input from team members involved in different stages of the pipeline. Their insights provide a holistic view of the process and encourage a culture of collaborative improvement.

Celebrating aAchievements

Acknowledge and celebrate achievements, big or small. Recognize the positive impact of implemented changes on DORA metrics. This boosts morale and reinforces a culture of continuous improvement.

Measure DORA metrics the Right Way!

To optimize DevOps practices and enhance organizational performance, organizations must master key metrics—Deployment Frequency, Lead Time for Changes, Mean Time to Recovery, and Change Failure Rate. Specialized tools like Typo simplify the measurement process, while GitLab's documentation aligns practices with industry standards. Successful DevOps teams prioritize continuous improvement through regular analysis, iterative adjustments, and adaptive responses. By using DORA metrics and committing to improvement, organizations can continuously elevate their performance.

Gain valuable insights and empower your engineering managers with Typo's robust capabilities.

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How to Build a DORA Metrics Dashboard?

In the rapidly evolving world of DevOps, it is essential to comprehend and improve your development and delivery workflows. To evaluate and enhance the efficiency of these workflows, the DevOps Research and Assessment (DORA) metrics serve as a crucial tool.

This blog, specifically designed for Typo, offers a comprehensive guide on creating a DORA metrics dashboard that will help you optimize your DevOps performance.

Why DORA metrics matter?

The DORA metrics consist of four key metrics:

Deployment frequency

Deployment frequency measures the frequency of deployment of code to production or releases to end-users in a given time frame.

Lead time

This metric measures the time between a commit being made and that commit making it to production.

Change failure rate

Change failure rate measures the proportion of deployment to production that results in degraded services.

Mean time to recovery

This metric is also known as the mean time to restore. It measures the time required to solve the incident i.e. service incident or defect impacting end-users.

These metrics provide valuable insights into the performance of your software development pipeline. By creating a well-designed dashboard, you can visualize these metrics and make informed decisions to improve your development process continuously.

How to build your DORA metrics dashboard?

Define your objectives

Before you choose a platform for your DORA Metrics Dashboard, it's important to first define clear and measurable objectives. Consider the Key Performance Indicators (KPIs) that align with your organizational goals. Whether it's improving deployment speed, reducing failure rates, or enhancing overall efficiency, having a well-defined set of objectives will help guide your implementation of the dashboard.

Selecting the right platform

When searching for a platform, it's important to consider your goals and requirements. Look for a platform that is easy to integrate, scalable, and customizable. Different platforms, such as Typo, have unique features, so choose the one that best suits your organization's needs and preferences.

Understanding DORA metrics

Gain a deeper understanding of the DevOps Research and Assessment (DORA) metrics by exploring the nuances of Deployment Frequency, Lead Time, Change Failure Rate, and MTTR. Then, connect each of these metrics with your organization's DevOps goals to have a comprehensive understanding of how they contribute towards improving overall performance and efficiency.

Dashboard configuration

After choosing a platform, it's important to follow specific guidelines to properly configure your dashboard. Customize the widgets to accurately represent important metrics and personalize the layout to create a clear and intuitive visualization of your data. This ensures that your team can easily interpret the insights provided by the dashboard and take appropriate actions.

Implementing data collection mechanisms

To ensure the accuracy and reliability of your DORA Metrics, it is important to establish strong data collection mechanisms. Configure your dashboard to collect real-time data from relevant sources, so that the metrics reflect the current state of your DevOps processes. This step is crucial for making informed decisions based on up-to-date information.

Integrating automation tools

To optimize the performance of your DORA Metrics Dashboard, you can integrate automation tools. By utilizing automation for data collection, analysis, and reporting processes, you can streamline routine tasks. This will free up your team's time and allow them to focus on making strategic decisions and improvements, instead of spending time on manual data handling.

Utilizing the dashboard effectively

To get the most out of your well-configured DORA Metrics Dashboard, use the insights gained to identify bottlenecks, streamline processes, and improve overall DevOps efficiency. Analyze the dashboard data regularly to drive continuous improvement initiatives and make informed decisions that will positively impact your software development lifecycle.

Challenges in building the DORA metrics dashboard

Data integration

Aggregating diverse data sources into a unified dashboard is one of the biggest hurdles while building the DORA metrics dashboard.

For example, if the metrics to be calculated is 'Lead time for changes' and sources include a version control system in GIT, Issue tracking in Jira, and a Build server in Jenkins. The timestamps recorded in Git, Jira, and Jenkins may not be synchronized or standardized and they may capture data at different levels of granularity.

Visualization and interpretation

Another challenge is whether the dashboard effectively communicates the insights derived from the metrics.

Suppose, you want to get visualized insights for deployment frequency. You choose a line chart for the same. However, if the frequency is too high, the chart might become cluttered and difficult to interpret. Moreover, displaying deployment frequency without additional information can lead to misinterpretation of the metric.

Cultural resistance

Teams may fear that the DORA dashboard will be used for blame rather than the improvement. Moreover, if there's a lack of trust in the organization, they question the motives behind implementing metrics and doubt the fairness of the process.

How Typo enhances your DevOps journey

Typo, as a dynamic platform, provides a user-friendly interface and robust features tailored for DevOps excellence.

Leveraging Typo for your DORA Metrics Dashboard offers several advantages:

DORA Metrics Dashboard

Tailored integration

It integrates with key DevOps tools, ensuring a smooth data flow for accurate metric representation.

Customization

It allows for easy customization of widgets, aligning the dashboard precisely with your organization's unique metrics and objectives.

Automation capabilities

Typo's automation features streamline data collection and reporting, reducing manual efforts and ensuring real-time, accurate insights.

Collaborative environment

It facilitates collaboration among team members, allowing them to collectively interpret and act upon dashboard insights, fostering a culture of continuous improvement.

Scalability

It is designed to scale with your organization's growth, accommodating evolving needs and ensuring the longevity of your DevOps initiatives.

When you opt for Typo as your preferred platform, you enable your team to fully utilize the DORA metrics. This drives efficiency, innovation, and excellence throughout your DevOps journey. Make the most of Typo to take your DevOps practices to the next level and stay ahead in the competitive software development landscape of today.

Conclusion

DORA metrics dashboard plays a crucial role in optimizing DevOps performance.

Building the dashboard with Typo provides various benefits such as tailored integration and customization. To know more about it, book your demo today!

The Dos and Don'ts of DORA Metrics

DORA Metrics assesses and enhances software delivery performance. Strategic considerations are necessary to identify areas of improvement, reduce time-to-market, and improve software quality. Effective utilization of DORA Metrics can drive positive organizational changes and achieve software delivery goals.

Dos of DORA Metrics

Understanding the Metrics

In 2015, The DORA team was founded by Gene Kim, Jez Humble, and Dr. Nicole Forsgren to evaluate and improve software development practices. The aim was to enhance the understanding of how organizations can deliver reliable and high-quality software faster.

To achieve success in the field of software development, it is crucial to possess a comprehensive understanding of DORA metrics. DORA, which stands for DevOps Research and Assessment, has identified four key DORA metrics critical in measuring and enhancing software development processes.

Four Key Metrics

  • Deployment Frequency: Deployment Frequency measures how frequently code changes are deployed into production.
  • Lead Time for Changes: Lead Time measures the time taken for a code change to be made and deployed into production.
  • Change Failure Rate: Change Failure Rate measures the percentage of code changes that fail in production.
  • Mean Time to Recover: Mean Time to Recover measures how long it takes to restore service after a failure.

Mastering these metrics is fundamental for accurately interpreting the performance of software development processes and identifying areas for improvement. By analyzing these metrics, DevOps teams can identify bottlenecks and inefficiencies, streamline their processes, and ultimately deliver reliable and high-quality software faster.

Alignment with Organizational Goals

The DORA (DevOps Research and Assessment) metrics are widely used to measure and improve software delivery performance. However, to make the most of these metrics, it is important to tailor them to align with specific organizational goals. By doing so, organizations can ensure that their improvement strategy is focused and impactful, addressing unique business needs.

Customizing DORA metrics requires a thorough understanding of the organization's goals and objectives, as well as its current software delivery processes. This may involve identifying the key performance indicators (KPIs) that are most relevant to the organization's specific goals, such as faster time-to-market or improved quality.

Once these KPIs have been identified, the organization can use DORA metrics data to track and measure its performance in these areas. By regularly monitoring these metrics, the organization can identify areas for improvement and implement targeted strategies to address them.

Regular Measurement and Monitoring

Consistency in measuring and monitoring DevOps Research and Assessment (DORA) metrics over time is essential for establishing a reliable feedback loop. This feedback loop enables organizations to make data-driven decisions, identify areas of improvement, and continuously enhance their software delivery processes. By measuring and monitoring DORA metrics consistently, organizations can gain valuable insights into their software delivery performance and identify areas that require attention. This, in turn, allows the organization to make informed decisions based on actual data, rather than intuition or guesswork. Ultimately, this approach helps organizations to optimize their software delivery pipelines and improve overall efficiency, quality, and customer satisfaction.

Promoting Collaboration

Using the DORA metrics as a collaborative tool can greatly benefit organizations by fostering shared responsibility between development and operations teams. This approach helps break down silos and enhances overall performance by improving communication and increasing transparency.

By leveraging DORA metrics, engineering teams can gain valuable insights into their software delivery processes and identify areas for improvement. These metrics can also help teams measure the impact of changes and track progress over time. Ultimately, using DORA metrics as a collaborative tool can lead to more efficient and effective software delivery and better alignment between development and operations teams.

Focus on Lead Time

Prioritizing the reduction of lead time involves streamlining the processes involved in the production and delivery of goods or services, thereby enhancing business value. By minimizing the time taken to complete each step, businesses can achieve faster delivery cycles, which is essential in today's competitive market.

This approach also enables organizations to respond more quickly and effectively to the evolving needs of customers. By reducing lead time, businesses can improve their overall efficiency and productivity, resulting in greater customer satisfaction and loyalty. Therefore, businesses need to prioritize the reduction of lead time if they want to achieve operational excellence and stay ahead of the curve.

Experiment and Iterate

When it comes to implementing DORA metrics, it's important to adopt an iterative approach that prioritizes adaptability and continuous improvement. By doing so, organizations can remain agile and responsive to the ever-changing technological landscape.

Iterative processes involve breaking down a complex implementation into smaller, more manageable stages. This allows teams to test and refine each stage before moving onto the next, which ultimately leads to a more robust and effective implementation.

Furthermore, an iterative approach encourages collaboration and communication between team members, which can help to identify potential issues early on and resolve them before they become major obstacles. In summary, viewing DORA metrics implementation as an iterative process is a smart way to ensure success and facilitate growth in a rapidly changing environment.

Celebrating Achievements

Recognizing and acknowledging the progress made in the DORA metrics is an effective way to promote a culture of continuous improvement within the organization. It not only helps boost the morale and motivation of the team but also encourages them to strive for excellence. By celebrating the achievements and progress made towards the goals, software teams can be motivated to work harder and smarter to achieve even better results.

Moreover, acknowledging improvements in key DORA metrics creates a sense of ownership and responsibility among the team members, which in turn drives them to take initiative and work towards the common goal of achieving organizational success.

Don'ts of DORA Metrics

Ignoring Context

It is important to note that drawing conclusions solely based on the metrics provided by the Declaration on Research Assessment (DORA) can sometimes lead to inaccurate or misguided results.

To avoid such situations, it is essential to have a comprehensive understanding of the larger organizational context, including its goals, objectives, and challenges. This contextual understanding empowers stakeholders to use DORA metrics more effectively and make better-informed decisions.

Therefore, it is recommended that DORA metrics be viewed as part of a more extensive organizational framework to ensure that they are interpreted and utilized correctly.

Overemphasizing Speed at the Expense of Stability

Maintaining a balance between speed and stability is crucial for the long-term success of any system or process. While speed is a desirable factor, overemphasizing it can often result in a higher chance of errors and a greater change failure rate.

In such cases, when speed is prioritized over stability, the system may become prone to frequent crashes, downtime, and other issues that can ultimately harm the overall productivity and effectiveness of the system. Therefore, it is essential to ensure that speed and stability are balanced and optimized for the best possible outcome.

Using Metrics for Blame

The DORA (DevOps Research and Assessment) metrics are widely used to measure the effectiveness and efficiency of software development teams covering aspects such as code quality and various workflow metrics. However, it is important to note that these metrics should not be used as a means to assign blame to individuals or teams.

Rather, they should be employed collaboratively to identify areas for improvement and to foster a culture of innovation and collaboration. By focusing on the collective goal of improving the software development process, teams can work together to enhance their performance and achieve better results.

It is crucial to approach DORA metrics as a tool for continuous improvement, rather than a means of evaluating individual performance. This approach can lead to more positive outcomes and a more productive work environment.

Neglecting Continuous Learning

Continuous learning, which refers to the process of consistently acquiring new knowledge and skills, is fundamental for achieving success in both personal and professional life. In the context of DORA metrics, which stands for DevOps Research and Assessment, it is important to consider the learning aspect to ensure continuous improvement.

Neglecting this aspect can impede ongoing progress and hinder the ability to keep up with the ever-changing demands and requirements of the industry. Therefore, it is crucial to prioritize learning as an integral part of the DORA metrics to achieve sustained success and growth.

Relying Solely on Benchmarking

Benchmarking is a useful tool for organizations to assess their performance, identify areas for improvement, and compare themselves to industry standards. However, it is important to note that relying solely on benchmarking can be limiting.

Every organization has unique circumstances that may require deviations from industry benchmarks. Therefore, it is essential to focus on tailored improvements that fit the specific needs of the organization. By doing so, software development teams can not only improve organizational performance but also achieve a competitive advantage within the industry.

Collecting Data without Action

To make the most out of data collection, it is crucial to have a well-defined plan for utilizing the data to drive positive change. The data collected should be relevant, accurate, and timely. The next step is to establish a feedback loop for analysis and implementation.

This feedback loop involves a continuous cycle of collecting data, analyzing it, making decisions based on the insights gained, and then implementing any necessary changes. This ensures that the data collected is being used to drive meaningful improvements in the organization.

The feedback loop should be well-structured and transparent, with clear communication channels and established protocols for data management. By setting up a robust feedback loop, organizations can derive maximum value from DORA metrics and ensure that their data collection efforts are making a tangible impact on their business operations.

Dismissing Qualitative Feedback

When it comes to evaluating software delivery performance and fostering a culture of continuous delivery, relying solely on quantitative data may not provide a complete picture. This is where qualitative feedback, particularly from engineering leaders, comes into play, as it enables us to gain a more comprehensive and nuanced understanding of how our software delivery process is functioning.

Combining both quantitative DORA metrics and qualitative feedback can ensure that continuous delivery efforts are aligned with the strategic goals of the organization. Hence, empowering engineering leaders to make informed, data-driven decisions that drive better outcomes.

Typo - A Leading DORA Metrics Tracker 

Typo is a powerful tool designed specifically for tracking and analyzing DORA metrics, providing an efficient solution for development teams to seek precision in their DevOps performance measurement.

  • With pre-built integrations in the dev tool stack, the DORA metrics dashboard provides all the relevant data flowing in within minutes.
  • It helps in deep diving and correlating different metrics to identify real-time bottlenecks, sprint delays, blocked PRs, deployment efficiency, and much more from a single dashboard.
  • The dashboard sets custom improvement goals for each team and tracks their success in real time.
  • It gives real-time visibility into a team’s KPI and lets them make informed decisions.

Align with DORA Metrics the Right Way

To effectively use DORA metrics and enhance developer productivity, organizations must approach them balanced with emphasis on understanding, alignment, collaboration, and continuous improvement. By following this approach, software teams can gain valuable insights to drive positive change and achieve engineering excellence with a focus on continuous delivery.

A holistic view of all aspects of software development helps identify key areas for improvement. Alignment ensures that everyone is working towards the same goals. Collaboration fosters communication and knowledge-sharing amongst teams. Continuous improvement is critical to engineering excellence, allowing organizations to stay ahead of the competition and deliver high-quality products and services to customers.

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Understanding DevOps and DORA Metrics: Transforming Software Development and Delivery

Adopting DevOps methods is crucial for firms aiming to achieve agility, efficiency, and quality in software development, which is a constantly changing terrain. The DevOps movement is both a cultural shift and a technological one; it promotes automation, collaboration, and continuous improvement among all parties participating in the software delivery lifecycle, from developers to operations.

The goal of DevOps is to improve software product quality, speed up development, and decrease time-to-market. Companies utilize metrics like DevOps Research and Assessment (DORA) to determine how well DevOps strategies are working and how to improve them.

The Essence of DevOps

DevOps is more than just a collection of methods; it's a paradigm change that encourages teams to work together, from development to operations. To accomplish common goals, DevOps practices eliminate barriers, enhance communication, and coordinate efforts. It guarantees consistency and dependability in software delivery and aims to automate processes to standardize and speed them up.

Foundational Concepts in DevOps:

  • Culture and Collaboration: Assisting teams in development, operations, and quality assurance to foster an environment of mutual accountability and teamwork.
  • Automation: automating mundane processes to make deployments more efficient and less prone to mistakes.
  • CI/CD pipelines: putting them in place to guarantee regular code integrations, testing, and quick deployment cycles.
  • Feedback loops : The importance of continual feedback loops for the quick detection and resolution of issues is emphasized in point four.

DORA Metrics: Assessing DevOps Performance

If you want to know how well your DevOps methods are doing, look no further than the DORA metrics.

DORA metrics, developed by the DORA team, are key performance indicators that measure the effectiveness and efficiency of software development and delivery processes. They provide a data-driven approach to evaluate the impact of operational practices on software delivery performance.

To help organizations find ways to improve and make smart decisions, these metrics provide quantitative insights into software delivery. Four key DORA metrics are Lead Time, Deployment Frequency, Change Failure Rate, and Mean Time to Recover. Let's read more about them in detail below:

Four Key DORA Metrics

Lead Time

The lead time is the sum of all the steps required to go from ideation to production deployment of a code update. All the steps involved are contained in this, including:

  • Collecting and analyzing requirements: Creating user stories, identifying requirements, and setting change priorities.
  • The development and testing phases include coding, feature implementation, and comprehensive testing.
  • Package the code, push it to production, and keep an eye on how it's doing—that's deployment and release.

Why Lead Time is important?

  • Improved iteration speeds: Users get new features and patches for bugs more often.
  • The group is more nimble and agile, allowing them to swiftly adjust to shifting consumer preferences and market conditions.
  • Increased productivity: finding and removing development process bottlenecks.
  • Customer satisfaction is increased because users enjoy a better experience because of speedier delivery of new products and upgrades.

Lead time can be affected by a number of things, such as:

  • Size of the team and level of expertise: A bigger team with more experienced members may do more tasks in less time.
  • The methodology of development: Agile approaches often result in reduced lead times when contrasted with more conventional waterfall processes.
  • Length of time required to design and test: The time required to develop and test more complicated features will inevitably increase the lead time.
  • Automation at a high level: Deploying and testing can be automated to cut down on lead time.

Optimizing lead time: Teams can actively work to reduce lead time by focusing on:

  • Facilitating effective handoffs of responsibilities and a shared knowledge of objectives are two ways in which team members can work together more effectively.
  • Workflow optimization: removing development process bottlenecks and superfluous stages.
  • To free up developer time for more valuable operations, automation tools can be used to automate repetitive chores.
  • Analyzing lead time: keeping tabs on lead time data on a regular basis and finding ways to make it better.

Deployment Frequency

Deployment Frequency measures how often changes to the code are pushed to production. Greater deployment frequency is an indication of increased agility and the ability to respond quickly to market demands. How often, in a specific time period, code updates are pushed to the production environment. A team can respond to client input, enhance their product, and supply new features and repairs faster with a greater Deployment Frequency.

Why Deployment Frequency is important?

  • More nimbleness and responsiveness to shifts in the market.
  • The feedback loop is faster and new features are brought to market faster.
  • Enhanced system stability and decreased risk for large-scale deployments.
  • Enhanced morale and drive within the team.

Approaches for maximizing the frequency of deployments:

  • Get rid of manual procedures and automate the deployment process.
  • Start CI/CD pipelines and make sure they're implemented.
  • Take advantage of infrastructure as code (IaC) to control the setup and provisioning of your infrastructure.
  • Minimize risk and rollback time by reducing deployment size.
  • Encourage team members to work together and try new things.

The choice between quality and stability and high Deployment Frequency should be carefully considered. Achieving success in the long run requires striking a balance between speed and quality. Optimal deployment frequencies will vary between teams and organizations due to unique requirements and limitations.

Change Failure Rate (CFR)

Change Failure Rate measures what proportion of changes fail or need quick attention after deployment. It helps you evaluate how well your testing and development procedures are working.

How to calculate CFR - Total unsuccessful changes divided by total deployed changes. To get a percentage, multiply by 100.

  • A low CFR indicates good code quality and testing techniques.
  • High CFR: Indicates code quality, testing, or change management concerns.

CFR Tracking Benefits

  • Better software quality by identifying high-failure areas for prioritizing development & testing enhancements.
  • Reduced downtime and expenses by preventing failures before production reduces downtime and costs.
  • Increased release confidence as a low CFR can help your team launch changes without regressions.

Approaches for CFR reduction

  • Implement rigorous testing (unit, integration, end-to-end tests) to find & fix errors early in development.
  • A fast and reliable CI/CD pipeline enables frequent deployments and early issue detection.
  • Focus on code quality by using code reviews, static code analysis, and other methods to improve code quality and maintainability.
  • Track CFR trends to identify areas for improvement and evaluate your adjustments.

Mean Time to Recover (MTTR)

MTTR evaluates the average production failure recovery time. Low MTTR means faster incident response and system resiliency. MTTR is an important system management metric, especially in production.

How to calculate MTTR : It is calculated by dividing the total time spent recovering from failures by the total number of failures over a specific period. After an incident, it estimates the average time to restore a system to normal.

Advantages from a low MTTR

  • Faster incident response reduces downtime and extends system availability.
  • Reduced downtime means less time lost due to outages, increasing production and efficiency.
  • Organizations may boost customer satisfaction and loyalty by reducing downtime and delivering consistent service.
  • Faster recoveries reduce downtime and maintenance costs, lowering outage costs.

Factors impact MTTR, including

  • Complexity: Complex situations take longer to diagnose and resolve.
  • Team Skills and Experience: Experienced teams diagnose and handle difficulties faster.
  • Available Resources: Having the right tools and resources helps speed recuperation.
  • Automating normal procedures reduces incident resolution manual labor.

Organizations can optimize MTTR with techniques like

  • Investing in incident response training and tools can help teams address incidents.
  • Conducting root cause analysis: Finding the cause of occurrences can avoid recurrence and speed rehabilitation.
  • Automating routine tasks: Automation can speed up incident resolution by reducing manual data collection, diagnosis, and mitigation.
  • Routine drills and simulations: Simulating incidents regularly helps teams improve their response processes.

Measuring DORA Effectively Requires Structure

  • Establish clear objectives and expected outcomes before adopting DORA measurements. Determine opportunities for improvement and connect metrics with goals.
  • Select Appropriate Tools: Use platforms that accurately record and evaluate metrics data. Monitoring tools, version control systems, and CI/CD pipelines may be used.
  • Set baseline values and realistic targets for improvement for each metric. Regularly evaluate performance against these benchmarks.
  • Foster Collaboration and Learning: Promote team collaboration and learning from metric data. Encourage suggestions for process improvements based on insights.
  • Iterate and Adapt: Continuous improvement is essential. Review and update measurements as business needs and technology change.

The adoption of DORA metrics brings several advantages to organizations:

Data-Driven Decision Making

  • DORA metrics provide concrete data points, replacing guesswork and assumptions. This data can be used to objectively evaluate past performance, identify trends, and predict future outcomes.
  • By quantifying successes and failures, DORA metrics enable informed resource allocation. Teams can focus their efforts on areas with the most significant potential for improvement.

Identifying Bottlenecks and Weaknesses

  • DORA metrics reveal areas of inefficiency within the software delivery pipeline. For example, a high mean lead time for changes might indicate bottlenecks in development or testing.
  • By pinpointing areas of weakness, DORA metrics help teams prioritize improvement initiatives and direct resources to where they are most needed.

Enhanced Collaboration

  • DORA metrics provide a common language and set of goals for all stakeholders involved in the software delivery process. This shared visibility promotes transparency and collaboration.
  • By fostering a culture of shared responsibility, DORA metrics encourage teams to work together towards achieving common objectives, leading to a more cohesive and productive environment.

Improved Time-to-Market

  • By optimizing processes based on data-driven insights from DORA metrics, engineering teams can significantly reduce the time it takes to deliver software to production.
  • This faster time-to-market allows organizations to respond rapidly to changing market demands and opportunities, giving them a competitive edge.

DORA Metrics and Value Stream Management

Value Stream Management refers to delivering frequent, high-quality releases to end-users. The success metric for value stream management is customer satisfaction i.e. realizing the value of the changes.

DORA DevOps metrics play a key role in value stream management as they offer baseline measures including:

  • Lead Time
  • Deployment Frequency
  • Change Failure Rate
  • Mean Time to Restore

By incorporating customer feedback, DORA metrics help DevOps teams identify potential bottlenecks and strategically position their services against competitors.

Industry Examples

E-Commerce Industry

Scenario: Improve Deployment Frequency and Lead Time

New features and updates must be deployed quickly in competitive e-commerce. E-commerce platforms can enhance deployment frequency and lead time with DORA analytics.

Example

An e-commerce company implements DORA metrics but finds that manual testing takes too long to deploy frequently. They save lead time and boost deployment frequency by automating testing and streamlining CI/CD pipelines. This lets businesses quickly release new features and upgrades, giving them an edge.

Finance Sector

Scenario: Reduce Change Failure Rate and MTTR

In the financial industry, dependability and security are vital, thus failures and recovery time must be minimized. DORA measurements can reduce change failures and incident recovery times.

Example

Financial institutions detect high change failure rates during transaction processing system changes. DORA metrics reveal failure causes including testing environment irregularities. Improvements in infrastructure as code and environment management reduce failure rates and mean time to recovery, making client services more reliable.

Healthcare Sector

Scenario: Increasing Deployment Time and CFR

In healthcare, where software directly affects patient care, deployment optimization and failure reduction are crucial. DORA metrics reduce change failure and deployment time.

Example

For instance, a healthcare software provider discovers that manual approval and validation slow rollout. They speed deployment by automating compliance checks and clarifying approval protocols. They also improve testing procedures to reduce change failure. This allows faster system changes without affecting quality or compliance, increasing patient care.

Tech Startups

Scenario: Accelerating deployment lead time

Tech businesses that want to grow quickly must provide products and upgrades quickly. DORA metrics improve deployment lead time.

Example

A tech startup examines DORA metrics and finds that manual configuration chores slow deployments. They automate configuration management and provisioning with infrastructure as code. Thus, their deployment lead time diminishes, allowing businesses to iterate and innovate faster and attract more users and investors.

Manufacturing Industry

Scenario: Streamlining Deployment Processes and Time

Even in manufacturing, where software automates and improves efficiency, deployment methods must be optimized. DORA metrics can speed up and simplify deployment.

Example

A manufacturing company uses IoT devices to monitor production lines in real time. However, updating these devices is time-consuming and error-prone. DORA measurements help them improve version control and automate deployment. This optimises production by reducing deployment time and ensuring more dependable and synchronised IoT device updates.

How does Typo leverage DORA Metrics for DevOps teams?

Typo is a leading AI-driven engineering analytics platform that provides SDLC visibility, data-driven insights, and workflow automation for software development teams. It provides comprehensive insights through DORA and other key metrics in a centralized dashboard.

‍Key Features

  • With pre-built integrations in the dev tool stack, the DORA metrics dashboard provides all the relevant data flowing in within minutes.
  • It helps in deep diving and correlating different metrics to identify real-time bottlenecks, sprint delays, blocked PRs, deployment efficiency, and much more from a single dashboard.
  • The dashboard sets custom improvement goals for each team and tracks their success in real-time.
  • It gives real-time visibility into a team’s KPI and lets them make informed decisions.
  • With the engineer benchmarking feature,  engineering leaders can overview industry-best benchmarks for each critical metric split across ‘Elite’, ‘High’, ‘Medium’ & ‘Needs Focus’ to compare the team's current performance.

Conclusion

Adopting DevOps and leveraging DORA metrics is crucial for modern software development. DevOps metrics drive collaboration and automation, while DORA metrics offer valuable insights to streamline delivery processes and boost team performance. Together, they help teams deliver higher-quality software faster and stay ahead in a competitive market.

What is the Change Failure Rate in DORA metrics?

Are you familiar with the term Change Failure Rate (CFR)? It's one of the key DORA metrics in DevOps that measures the percentage of failed changes out of total implementations. This metric is pivotal for development teams in assessing the reliability of the deployment process.

What is the Change Failure Rate?

CFR, or Change Failure Rate metric measures the frequency at which newly deployed changes lead to failures, glitches, or unexpected outcomes in the IT environment. It reflects the stability and reliability of the entire software development and deployment lifecycle. By tracking CFR, teams can identify bottlenecks, flaws, or vulnerabilities in their processes, tools, or infrastructure that can negatively impact the quality, speed, and cost of software delivery.

Lowering CFR is a crucial goal for any organization that wants to maintain a dependable and efficient deployment pipeline. A high CFR can have serious consequences, such as degraded service, delays, rework, customer dissatisfaction, revenue loss, or even security breaches. To reduce CFR, teams need to implement a comprehensive strategy involving continuous testing, monitoring, feedback loops, automation, collaboration, and culture change. By optimizing their workflows and enhancing their capabilities, teams can increase agility, resilience, and innovation while delivering high-quality software at scale.

Screenshot 2024-03-16 at 1.16.22 AM.png

How to Calculate Change Failure Rate?

Change failure rate measures software development reliability and efficiency. It’s related to team capacity, code complexity, and process efficiency, impacting speed and quality. Change Failure Rate calculation is done by following these steps:

Identify Failed Changes: Keep track of the number of changes that resulted in failures during a specific timeframe.

Determine Total Changes Implemented: Count the total changes or deployments made during the same period.

Apply the formula:

Use the formula CFR = (Number of Failed Changes / Total Number of Changes) * 100 to calculate the Change Failure Rate as a percentage.

Here is an example: Suppose during a month:

Failed Changes = 5

Total Changes = 100

Using the formula: (5/100)*100 = 5

Therefore, the Change Failure Rate for that period is 5%.

 

Change failure rate

Elite performers

0% – 15%

High performers

0% – 15%

Medium performers

15% – 45%

Low performers

45% – 60%

It only considers what happens after deployment and not anything before it. 0% - 15% CFR is considered to be a good indicator of your code quality.

Low change failures mean that the code review and deployment process needs attention. To reduce it, the team should focus on reducing deployment failures and time wasted due to delays, ensuring a smoother and more efficient software delivery performance.

With Typo, you can improve dev efficiency and team performance with an inbuilt DORA metrics dashboard.

  • With pre-built integrations in your dev tool stack, get all the relevant data flowing in within minutes and see it configured as per your processes. 
  • Gain visibility beyond DORA by diving deep and correlating different metrics to identify real-time bottlenecks, sprint delays, blocked PRs, deployment efficiency, and much more from a single dashboard.
  • Set custom improvement goals for each team and track their success in real time. Also, stay updated with nudges and alerts in Slack. 

Use Cases

Stability is pivotal in software deployment. The change Failure Rate measures the percentage of changes that fail. A high failure rate could signify inadequate testing, poor code quality, or insufficient quality control. Enhancing testing protocols, refining the code review process, and ensuring thorough documentation can reduce the failure rate, enhancing overall stability and team performance.

Code Review Excellence

Metrics: Comments per PR and Change Failure Rate

Few Comments per PR, Low Change Failure Rate

Low comments and minimal deployment failures signify high-quality initial code submissions. This scenario highlights exceptional collaboration and communication within the team, resulting in stable deployments and satisfied end-users.

Abundant Comments per PR, Minimal Change Failure Rate

Teams with numerous comments per PR and a few deployment issues showcase meticulous review processes. Investigating these instances ensures review comments align with deployment stability concerns, ensuring constructive feedback leads to refined code.

The Essence of Change Failure Rate

Change Failure Rate (CFR) is more than just a metric and is an essential indicator of an organization's software development health. It encapsulates the core aspects of resilience and efficiency within the software development life cycle.

Reflecting Organizational Resilience

The CFR (Change Failure Rate) reflects how well an organization's software development practices can handle changes. A low CFR indicates the organization can make changes with minimal disruptions and failures. This level of resilience is a testament to the strength of their processes, showing their ability to adapt to changing requirements without difficulty.

Efficiency in Deployment Processes

Efficiency lies at the core of CFR. A low CFR indicates that the organization has streamlined its deployment processes. It suggests that changes are rigorously tested, validated, and integrated into the production environment with minimal disruptions. This efficiency is not just a numerical value, but it reflects the organization's dedication to delivering dependable software.

Early Detection of Potential Issues

A high change failure rate, on the other hand, indicates potential issues in the deployment pipeline. It serves as an early warning system, highlighting areas that might affect system reliability. Identifying and addressing these issues becomes critical in maintaining a reliable software infrastructure.

Impact on Overall System Reliability

The essence of CFR (Change Failure Rate) lies in its direct correlation with the overall reliability of a system. A high CFR indicates that changes made to the system are more likely to result in failures, which could lead to service disruptions and user dissatisfaction. Therefore, it is crucial to understand that the essence of CFR is closely linked to the end-user experience and the trustworthiness of the deployed software.

Change Failure Rate and its Importance with Organization Performance

The Change Failure Rate (CFR) is a crucial metric that evaluates how effective an organization's IT practices are. It's not just a number - it affects different aspects of organizational performance, including customer satisfaction, system availability, and overall business success. Therefore, it is important to monitor and improve it.

Assessing IT Health

Key Performance Indicator

Efficient IT processes result in a low CFR, indicating a reliable software deployment pipeline with fewer failed deployments.

Identifying Weaknesses

Organizations can identify IT weaknesses by monitoring CFR. High CFR patterns highlight areas that require attention, enabling proactive measures for software development.

Correlation with Organizational Performance

Customer Satisfaction

CFR directly influences customer satisfaction. High CFR can cause service issues, impacting end-users. Low CFR results in smooth deployments, enhancing user experience.

System Availability

The reliability of IT systems is critical for business operations. A lower CFR implies higher system availability, reducing the chances of downtime and ensuring that critical systems are consistently accessible.

Influence on Overall Business Success

Operational Efficiency

Efficient IT processes are reflected in a low CFR, which contributes to operational efficiency. This, in turn, positively affects overall business success by streamlining development workflows and reducing the time to market for new features or products.

Cost Savings

A lower CFR means fewer post-deployment issues and lower costs for resolving problems, resulting in potential revenue gains. This financial aspect is crucial to the overall success and sustainability of the organization.

Proactive Issue Resolution

Continuous Improvement

Organizations can improve software development by proactively addressing issues highlighted by CFR.

Maintaining a Robust IT Environment

Building Resilience

Organizations can enhance IT resilience by identifying and mitigating factors contributing to high CFR.

Enhancing Security

CFR indirectly contributes to security by promoting stable and reliable deployment practices. A well-maintained CFR reflects a disciplined approach to changes, reducing the likelihood of introducing vulnerabilities into the system.

Strategies for Optimizing Change Failure Rate

Implementing strategic practices can optimize the Change Failure Rate (CFR) by enhancing software development and deployment reliability and efficiency.

Automation

Automated Testing and Deployment

Implementing automated testing and deployment processes is crucial for minimizing human error and ensuring the consistency of deployments. Automated testing catches potential issues early in the development cycle, reducing the likelihood of failures in production.

Continuous Integration (CI) and Continuous Deployment (CD)

Leverage CI/CD pipelines for automated integration and deployment of code changes, streamlining the delivery process for more frequent and reliable software updates.

Continuous monitoring

Real-Time Monitoring

Establishing a robust monitoring system that detects issues in real time during the deployment lifecycle is crucial. Continuous monitoring provides immediate feedback on the performance and stability of applications, enabling teams to promptly identify and address potential problems.

Alerting Mechanisms

Implement mechanisms to proactively alert relevant teams of anomalies or failures in the deployment pipeline. Swift response to such notifications can help minimize the potential impact on end-users.

Collaboration

DevOps Practices

Foster collaboration between development and operations teams through DevOps practices. Encourage cross-functional communication and shared responsibilities to create a unified software development and deployment approach.

Communication Channels

Efficient communication channels & tools facilitate seamless collaboration, ensuring alignment & addressing challenges.

Iterative Improvements

Feedback Loops

Create feedback loops in development and deployment. Collect feedback from the team, and users, and monitor tools for improvement.

Retrospectives

It's important to have regular retrospectives to reflect on past deployments, gather insights, and refine deployment processes based on feedback. Strive for continuous improvement.

Improve Change Failure Rate for Your Engineering Teams

Empower software development teams with tools, training, and a culture of continuous improvement. Encourage a blame-free environment that promotes learning from failures. CFR is one of the key metrics and critical performance metrics of DevOps maturity. Understanding its implications and implementing strategic optimizations is a great way to enhance deployment processes, ensuring system reliability and contributing to business success.

Typo provides an all-inclusive solution if you're looking for ways to enhance your team's productivity, streamline their work processes, and build high-quality software for end-users.

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What is the Lead Time for Changes in DORA Metrics?

Understanding and optimizing key metrics is crucial in the dynamic landscape of software development. One such metric, Lead Time for Changes, is a pivotal factor in the DevOps world. Let's delve into what this metric entails and its significance in the context of DORA (DevOps Research and Assessment) metrics.

What is the Lead Time for Changes?

Lead Time for Changes is a critical metric used to measure the efficiency and speed of software delivery. It is the duration between a code change being committed and its successful deployment to end-users.

The measurement of this metric offers valuable insights into the effectiveness of development processes, deployment pipelines, and release strategies. By analyzing the Change lead time, development teams can identify bottlenecks in the delivery pipeline and streamline their workflows to improve software delivery's overall speed and efficiency. Therefore, it is crucial to track and optimize this metric.

How to calculate Lead Time for Changes?

This metric is a good indicator of the team’s capacity, code complexity, and efficiency of the software development process. It is correlated with both the speed and quality of the engineering team, which further impacts cycle time.

Lead time for changes measures the time that passes from the first commit to the eventual deployment of code.

To measure this metric, DevOps should have:

  • The exact time of the commit 
  • The number of commits within a particular period
  • The exact time of the deployment 

Divide the total sum of time spent from commitment to deployment by the number of commitments made. Suppose, the total amount of time spent on a project is 48 hours. The total number of commits made during that time is 20. This means that the lead time for changes would be 2.4 hours. In other words, an average of 2.4 hours are required for a team to make changes and progress until deployment time.

 

Lead time for change

Elite performers

Less than 1 hour

High performers

Between 1 hour and 1 week

Medium performers

Between 1 week and 6 months

Low performers

More than or equal to 6 months

A shorter lead time means more efficient a DevOps team is in deploying code, differentiating elite performers from low performers.

Longer lead times can signify the testing process is obstructing the CI/CD pipeline. It can also limit the business’s ability to deliver value to the end users. Hence, install more automated deployment and review processes. It further divides production and features into much more manageable units.

With Typo, you can improve dev efficiency with an inbuilt DORA metrics dashboard.

  • With pre-built integrations in your dev tool stack, get all the relevant data flowing in within minutes and see it configured as per your processes. 
  • Gain visibility beyond DORA by diving deep and correlating different metrics to identify real-time bottlenecks, sprint delays, blocked PRs, deployment efficiency, and much more from a single dashboard.
  • Set custom improvement goals for each team and track their success in real-time. Also, stay updated with nudges and alerts in Slack. 

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Use cases

Picture your software development team tasked with a critical security patch. Measuring change lead time helps pinpoint the duration from code commit to deployment. If it goes for a long run, bottlenecks in your CI/CD pipelines or testing processes might surface. Streamlining these areas ensures rapid responses to urgent tasks.

Development Cycle Efficiency

Metrics: Lead Time for Changes and Deployment Frequency

High Deployment Frequency, Swift Lead Time

Teams with rapid deployment frequency and short lead time exhibit agile development practices. These efficient processes lead to quick feature releases and bug fixes, ensuring dynamic software development aligned with market demands and ultimately enhancing customer satisfaction.

Low Deployment Frequency despite Swift Lead Time

A short lead time coupled with infrequent deployments signals potential bottlenecks. Identifying these bottlenecks is vital. Streamlining deployment processes in line with development speed is essential for a software development process.

Impact of PR Size on Lead Time for Changes

The size of a pull request (PR) profoundly influences overall lead time. Large PRs require more review time hence delaying the process of code review adding to the overall lead time (longer lead times). Dividing large tasks into manageable portions accelerates deployments, and reduces deployment time addressing potential bottlenecks effectively.

The essence of Lead Time for Changes

At its core, a mean lead time for Changes of the entire development process reflects its agility. It encapsulates the entire journey of a code change, from conception to production, offering insights into workflow efficiency and identifying potential bottlenecks.

Agility and Development Processes

Agility is a crucial aspect of software development that enables organizations to keep up with the ever-evolving landscape. It is the ability to respond swiftly and effectively to changes while maintaining a balance between speed and stability in the development life cycle. Agility can be achieved by implementing flexible processes, continuous integration and continuous delivery, automated testing, and other modern development practices that enable software development teams to pivot and adapt to changing business requirements quickly.

Organizations that prioritize agility are better equipped to handle unexpected challenges, stay ahead of competitors, and deliver high-quality software products that meet the needs of their customers.

End-to-End Journey

The development pipeline has several stages: code initiation, development, testing, quality assurance, and final deployment. Each stage is critical for project success and requires attention to detail and coordination. Code initiation involves planning and defining the project.

Development involves coding, testing, and collaboration. Testing evaluates the software, while quality assurance ensures it's bug-free. Final deployment releases the software. This pipeline provides a comprehensive view of the process for thorough analysis.

Insights into Efficiency

Measuring the duration of each stage of development is a critical aspect of workflow analysis. Quantifying the time taken by each stage makes it possible to identify areas where improvements can be made to streamline processes and reduce unnecessary delays.

This approach offers a quantitative measure of the efficiency of each workflow, highlighting areas that require attention and improvement. By tracking the time taken at each stage, it is possible to identify bottlenecks and other inefficiencies that may be affecting the overall performance of the workflow. This information can then be used to develop strategies for improving workflow efficiency, reducing costs, and improving the final product or service quality.

Identifying Bottlenecks

It can diagnose and identify specific stages or processes causing system delays. It helps devops teams to proactively address bottlenecks by providing detailed insights into the root causes of delays. By identifying these bottlenecks, teams can take corrective action to enhance overall efficiency and reduce lead time.

It is particularly useful in complex systems where delays may occur at multiple stages, and pinpointing the exact cause of a delay can be challenging. With this tool, teams can quickly and accurately identify the source of the bottleneck and take corrective action to improve the system's overall performance.

Lead Time for Changes and its importance with organization performance

The importance of Lead Time for Changes cannot be overstated. It directly correlates with an organization's performance, influencing deployment frequency and the overall software delivery performance. A shorter lead time enhances adaptability, customer satisfaction, and competitive edge.

Correlation with Performance

Short lead times have a significant impact on an organization's performance. They allow organizations to respond quickly to changing market conditions and customer demands, improving time-to-market, customer satisfaction, and operational efficiency.

Influencing Deployment Frequency

Low lead times in software development allow high deployment frequency, enabling rapid response to market demands and improving the organization's ability to release updates, features, and bug fixes. This helps companies stay ahead of competitors, adapt to changing market conditions, and reduce the risks associated with longer development cycles.

Enhanced Velocity

High velocity is essential for the software delivery performance. By streamlining the process, improving collaboration, and removing bottlenecks, new features and improvements can be delivered quickly, resulting in better user experience and increased customer satisfaction. A high delivery velocity is essential for remaining competitive.

Adaptability and Customer Satisfaction

Shorter lead times have a significant impact on organizational adaptability and customer satisfaction. When lead times are reduced, businesses can respond more quickly to changes in the market, customer demands, and internal operations. This increased agility allows companies to make adjustments faster and with less risk, improving customer satisfaction.

Additionally, shorter lead times can lower inventory costs and improve cash flow, as businesses can more accurately forecast demand and adjust their production and supply chain accordingly. Overall, shorter lead times are a key factor in building a more efficient and adaptable organization.

Competitive Edge

To stay competitive, businesses must minimize lead time. This means streamlining software development, optimizing workflows, and leveraging automation tools to deliver products faster, cut costs, increase customer satisfaction, and improve the bottom line.

Strategies for Optimizing Lead Time for Changes

Organizations can employ various strategies to optimize Lead Time for Changes. These may include streamlining development workflows, adopting automation, and fostering a culture of continuous improvement.

Streamlining Workflows

The process of development optimization involves analyzing each stage of the development process to identify and eliminate any unnecessary steps and delays. The ultimate goal is to streamline the process and reduce the time it takes to complete a project. This approach emphasizes the importance of having a well-defined and efficient workflow, which can improve productivity, increase efficiency, and reduce the risk of errors or mistakes. By taking a strategic and proactive approach to development optimization, businesses can improve their bottom line by delivering projects more quickly and effectively while also improving customer satisfaction and overall quality.

Adopting Automation

Automation tools play a crucial role in streamlining workflows, especially when it comes to handling repetitive and time-consuming tasks. With the help of automation tools, businesses can significantly reduce manual intervention, minimize the likelihood of errors, and speed up their development cycle.

By automating routine tasks such as data entry, report generation, and quality assurance, employees can focus on more strategic and high-value activities, leading to increased productivity and efficiency. Moreover, automation tools can be customized to fit the specific needs of a business or a project, providing a tailored solution to optimize workflows.

Faster Feedback and Continuous Improvement Culture

Regular assessment and enhancement of development processes are crucial for maintaining high-performance levels. This promotes continual learning and adaptation to industry best practices, ensuring software development teams stay up-to-date with the latest technologies and methodologies. By embracing a culture of continuous improvement, organizations can enhance efficiency, productivity, and competitive edge.

Regular assessments and faster feedback allow teams to identify and address inefficiencies, reduce lead time for changes, and improve software quality. This approach enables organizations to stay ahead by adapting to changing market conditions, customer demands, and technological advancements.

Improve Lead Time for Changes for your Engineering Teams

Lead Time for Changes is a critical metric within the DORA framework. Its efficient management directly impacts an organization's competitiveness and ability to meet market demands. Embracing optimization strategies ensures a speedier software delivery process and a more resilient and responsive development ecosystem.

We have a comprehensive solution if you want to increase your development team's productivity and efficiency.

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What is Deployment Frequency in DORA Metrics?

In today's fast-paced software development industry, measuring and enhancing the efficiency of development processes is becoming increasingly important. The DORA Metrics framework has gained significant attention, and one of its essential components is Development Frequency. This blog post aims to comprehensively understand this metric by delving into its significance, impact on the organization's performance, and deployment optimization strategies.

What is Deployment Frequency?

In the world of DevOps, the Deployment Frequency metric reigns supreme. It measures the frequency of code deployment to production and reflects an organization's efficiency, reliability, and software delivery quality. By achieving an optimal balance between speed and stability, organizations can achieve agility, efficiency, and a competitive edge.But Development Frequency is more than just a metric; it's a catalyst for continuous delivery and iterative development practices that align seamlessly with the principles of DevOps. It helps organizations maintain a balance between speed and stability, which is a recurring challenge in software development.When organizations achieve a high Development Frequency, they can enjoy rapid releases without compromising the software's robustness. This can be a powerful driver of agility and efficiency, making it an essential component of software development.

How to Calculate Deployment Frequency?

Deployment frequency is often used to track the rate of change in software development and to highlight potential areas for improvement. It is important to measure Deployment Frequency for the following reasons:

  • It provides insights into the overall efficiency and speed of the development team’s processes. Besides this, Deployment Frequency also highlights the stability and reliability of the production environment. 
  • It helps in identifying pitfalls and areas for improvement in the software development life cycle. 
  • It helps in making data-driven decisions to optimize the process. 
  • It helps in understanding the impact of changes on system performance. 

Deployment Frequency is measured by dividing the number of deployments made during a given period by the total number of weeks/days. For example: If a team deployed 6 times in the first week, 7 in the second week, 4 in the third week, and 7 in the fourth week. Then, the deployment frequency is 6 per week.

 

Deployment frequency

Elite performers

On-demand (Multiple deploys per day)

High performers

More than 1 deployment/week and less than 1 month

Medium performers

More than 1 deployment/month and less than ⅙ months 

Low performers

Less than 1 deployment/6 months

One deployment per week is standard. However, it also depends on the type of product.

Teams that fall under the low performers category can install more automated processes. Such as for testing and validating new code and minimizing the time span between error recovery time and delivery.

Note that this is the first key metric. If the team takes the wrong approach in the first step, it can lead to the degradation of other DORA metrics as well.

With Typo, you can improve dev efficiency with DORA metrics.

  • With pre-built integrations in your dev tool stack, get all the relevant data flowing in within minutes and see it configured as per your processes. 
  • Gain visibility beyond DORA by diving deep and correlating different metrics to identify real-time bottlenecks, sprint delays, blocked PRs, deployment efficiency, and much more from a single dashboard.
  • Set custom improvement goals for each team and track their success in real-time. Also, stay updated with nudges and alerts in Slack. 

What are the Other Methods for Calculating Deployment Frequency?

There are various ways to calculate Deployment Frequency. These include :

Counting the Number of Deployments

One of the easiest ways to calculate Deployment Frequency is by counting the number of deployments in a given time period. It can be done either by manually counting the number of deployments or by using a tool to calculate deployments such as a version control system or deployment pipeline.

Measuring the Deployment Time

Deployment Frequency can also be calculated by measuring the time it takes for code changes to be deployed in production. It can be done in two ways:

  • Measuring the time from when code is committed to when it is deployed
  • Measuring the time from when a deployment is initiated to when it is completed

Measuring the Rate of Deployments

The deployment rate can be measured by the number of deployments per unit of time including deployments per day or per week. This can be dependent on the rhythm of your development and release cycles.

A/B Testing

Another way of measuring Deployment Frequency is by counting the number of A/B tests launched during a given time period.

The Essence of Development Frequency

Speed and Stability

Achieving a balance between fast software releases and maintaining a stable software environment is a subtle skill. It requires a thorough understanding of trade-offs and informed decision-making to optimize both. Development Frequency enables organizations to achieve faster release cycles, allowing them to respond promptly to market demands, while ensuring the reliability and integrity of their software.

Reducing Lead Time

Frequent software development plays a crucial role in reducing lead time and allows organizations to respond quickly to market dynamics and customer feedback. The ability to frequently deploy software enhances an organization's adaptability to market demands and ensures swift responses to valuable customer feedback.

Continuous Improvement

Development Frequency cultivates a culture of constant improvement by following iterative software development practices. Accepting change as a standard practice rather than an exception is encouraged. Frequent releases enable quicker feedback loops, promoting a culture of learning and adaptation. Detecting and addressing issues at an early stage and implementing effective iterations become an integral part of the development process.

Impact on Organizational Performance

Business Agility

Frequent software development is directly linked to improved business agility. This means that organizations that develop and deploy software more often are better equipped to respond quickly to changes in the market and stay ahead of the competition.

With frequent deployments, organizations can adapt and meet the needs of their customers with ease, while also taking advantage of new opportunities as they arise. This adaptability is crucial in today's fast-paced business environment, and it can help companies stay competitive and successful.

Quality Assurance

High Development Frequency does not compromise software quality. Instead, it often leads to improved quality by dispelling misconceptions associated with infrequent deployments. Emphasizing the role of Continuous Integration, Continuous Deployment (CI/CD), automated testing, and regular releases elevates software quality standards.

Strategies for Optimizing Development frequency

Automation and CI/CD

Having a robust automation process, especially through Continuous Integration/Continuous Delivery (CI/CD) pipelines, is a critical factor in optimizing Development Frequency. This process helps streamline workflows, minimize manual errors, and accelerate release cycles. CI/CD pipelines are the backbone of software development as they automate workflows and enhance the overall efficiency and reliability of the software delivery pipeline.

Microservices Architecture

Microservices architecture promotes modularity by design. This architectural choice facilitates independent deployment of services and aligns seamlessly with the principles of high development frequency. The modular nature of microservices architecture enables individual component releases, ensuring alignment with the goal of achieving high development frequency.

Feedback Loops and Monitoring

Efficient feedback loops are essential for the success of Development Frequency. They enable rapid identification of issues, enabling timely resolutions. Comprehensive monitoring practices are critical for identifying and resolving issues. They significantly contribute to maintaining a stable and reliable development environment.

Reinforce the Importance of Engineering Teams

Development Frequency is not just any metric; it's the key to unlocking efficient and agile DevOps practices. By optimizing your development frequency, you can create a culture of continuous learning and adaptation that will propel your organization forward. With each deployment, iteration, and lesson learned, you'll be one step closer to a future where DevOps is a seamless, efficient, and continuously evolving practice. Embrace the frequency, tackle the challenges head-on, and chart a course toward a brighter future for your organization.

If you are looking for more ways to accelerate your dev team’s productivity and efficiency, we have a comprehensive solution for you.

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9 KPIs to Help Your Software Development Team Succeed

Key Performance Indicators (KPIs) are the informing factors and draw paths for teams in the dynamic world of software development, where growth depends on informed decisions and concentrated efforts. In this in-depth post, we explore the fundamental relevance of software development KPIs and how to recognize, pick, and effectively use them.

What are Software Development KPIs?

Key performance indicators are the compass that software development teams use to direct their efforts with purpose, enhance team productivity, measure their progress, identify areas for improvement, and ultimately plot their route to successful outcomes. Software development metrics while KPIs add context and depth by highlighting the measures that align with business goals.

Benefits of Using KPIs

Using key performance indicators is beneficial for both team members and organizations. Below are some of the benefits of KPIs:

Efficient Continuous Delivery

Key performance indicator such as cycle time helps in optimizing continuous delivery processes. It further assists in streamlining development, testing, and deployment workflows. Hence, resulting in quicker and more reliable feature releases.

Resource Utilization Optimization

KPIs also highlight resource utilization patterns. Engineering leaders can identify if team members are overutilized or underutilized. This helps in allowing for better resource allocation to avoid burnout and to balance the workloads.

Prioritization of New Features

KPIs assist in prioritizing new features effectively. Through these, software engineers and developers can identify which features contribute the most to key objectives.

Knowing the Difference Between Metrics and KPIs

In software development, KPIs and software metrics serve as vital tools for software developers and engineering leaders to keep track of their processes and outcomes.

It is crucial to distinguish software metrics from KPIs. While KPIs are the refined insights drawn from the data and polished to coincide with the broader objectives of a business, metrics are the raw, unprocessed information. Tracking the number of lines of code (LOC) produced, for example, is only a metric; raising it to the status of a KPI for software development teams falls short of understanding the underlying nature of progress.

Focus

  • Metrics' key focus is on gathering data related to different development aspects.
  • KPIs shed light on the most critical performance indicators.

Strategic Alignment

  • Software metrics offer quantitative data about various aspects of the software process.
  • KPIs are chosen to align directly with strategic objectives and primary business goals.

Actionable Insights

  • Metrics are used for monitoring purposes. However, they aren't directly tied to strategic objectives,
  • Software development KPIs provide actionable insights that guide the development team toward specific actions or improvements.

The Crucial Role of Selecting the Right KPIs

Selecting the right KPIs requires careful consideration. It's not just about analyzing data, but also about focusing your team's efforts and aligning with your company's objectives.

Choosing KPIs must be strategic, intentional, and shaped by software development fundamentals. Here is a helpful road map to help you find your way:

Teamwork Precedes Solo Performance

Collaboration is at the core of software development. KPIs should highlight team efficiency as a whole rather than individual output. The symphony, not the solo, makes a work of art.

Put quality Before Quantity

Let quality come first. The dimensions of excellence should be explored in KPIs. Consider measurements that reflect customer happiness or assess the efficacy of non-production testing rather than just adding up numbers.

Sync KPIs with Important Processes

Introspectively determine your key development processes before choosing KPIs. Let the KPIs reflect these crucial procedures, making them valuable indications rather than meaningless measurements.

Beware of Blind Replication

Mindlessly copying KPIs may be dangerous, even if learning from others is instructive. Create KPIs specific to your team's culture, goals, and desired trajectory.

Obtain Team Agreement

Team agreement is necessary for the implementation of KPIs. The KPIs should reflect the team's priorities and goals and allow the team to own its course. It also helps in increasing team morale and productivity.

Start with Specific KPIs

To make a significant effect, start small. Instead of overloading your staff with a comprehensive set of KPIs, start with a narrow cluster and progressively add more as you gain more knowledge.

9 KPIs for Software Development

These nine software development KPIs go beyond simple measurements and provide helpful information to advance your development efforts.

Team Induction Time: Smooth Onboarding for Increased Productivity

The induction period for new members is crucial in the fire of collaboration. Calculate how long it takes a beginner to develop into a valuable contributor. A shorter induction period and an effective learning curve indicate a faster production infusion. Swift integration increases team satisfaction and general effectiveness, highlighting the need for a well-rounded onboarding procedure.

Effective onboarding may increase employee retention by 82%, per a Glassdoor survey. A new team member is more likely to feel appreciated and engaged when integrated swiftly and smoothly, increasing productivity.

Effectiveness Testing: Strengthening Quality Assurance

Strong quality assurance is necessary for effective software. Hence, testing efficiency is a crucial KPI. Merge metrics for testing branch coverage, non-production bugs, and production bugs. The objective is to develop robust testing procedures that eliminate manufacturing flaws, Improve software quality, optimize procedures, spot bottlenecks, and avoid problems after deployment by evaluating the effectiveness of pre-launch evaluations.

A Consortium for IT Software Quality (CISQ) survey estimates that software flaws cost the American economy $2.84 trillion yearly. Effective testing immediately influences software quality by assisting in defect mitigation and lowering the cost impact of software failures.

Effective Development: The Art of Meaningful Code Changes

The core of efficient development is beyond simple code production; it is an art that takes the form of little rework, impactful code modifications, and minimal code churn. Calculate the effectiveness of code modifications and strive to produce work beyond output and representing impact. This KPI celebrates superior coding and highlights the inherent worth of pragmatistically considerate coding.

In 2020, the US incurred a staggering cost of approximately $607 billion due to software bugs, as reported by Herb Kranser in "The Cost of Poor Software Quality in the US. Effective development immediately contributes to cost reduction and increased software quality, as seen by less rework, effective coding, and reduced code churn.

Customer Satisfaction: Highlighting the Triumph of the User

The user experience is at the center of software development. It is crucial for quality software products, engineering teams, and project managers. With surgical accuracy, assess user happiness. Metrics include feedback surveys, use statistics, and the venerable Net Promoter Score (NPS). These measurements combine to reveal your product's resonance with its target market. By decoding user happiness, you can infuse your development process with meaning and ensure alignment with user demands and corporate goals. These KPIs can also help in improving customer retention rates.

According to a PwC research, 73% of consumers said that the customer experience heavily influences their buying decisions. The success of your software on the market is significantly impacted by how well you can evaluate user happiness using KPIs like NPS.

Cycle Time: Managing Agile Effectiveness

Cycle time is the main character in the complex ballet that is development. Describe the process from conception to deployment in production. The tangled paths of planning, designing, coding, testing, and delivery are traversed by this KPI. Spotting bottlenecks facilitates process improvement, and encouraging agility allows accelerated results.Cycle time reflects efficiency and is essential for achieving lean and effective operations. In line with agile principles, cycle time optimization enables teams to adapt more quickly to market demands and provide value more often.

Promoting Reliability in the Face of Complexity: Production Stability and Observability

Although no program is impervious to flaws, stability and observability are crucial. Watch the Mean Time To Detect (MTTD), Mean Time To Recover (MTTR), and Change Failure Rate (CFR). This trio (the key areas of DORA metrics) faces the consequences of manufacturing flaws head-on. Maintain stability and speed up recovery by improving defect identification and action. This KPI protects against disruptive errors while fostering operational excellence.

Increased deployment frequency and reduced failure rates are closely correlated with focusing on production stability and observability in agile software development.

Fostering a Healthy and Satisfied Team Environment for a Successful Development Ecosystem

A team's happiness and well-being are the cornerstones of long-term success. Finding a balance between meeting times and effective work time prevents fatigue. A happy, motivated staff enables innovation. Prioritizing team well-being and happiness in the post-pandemic environment is not simply a strategy; it is essential for excellence in sustainable development.

Happy employees are also 20% more productive! Therefore, monitoring team well-being and satisfaction using KPIs like the meeting-to-work time ratio ensures your workplace is friendly and productive.

Documentation and Knowledge Exchange: Using Transfer of Wisdom to Strengthen Resilience

The software leaves a lasting impact that transcends humans. Thorough documentation prevents knowledge silos. To make transitions easier, measure the coverage of the code and design documentation. Each piece of code that is thoroughly documented is an investment in continuity. Protecting collective wisdom supports unbroken development in the face of team volatility as the industry thrives on evolution.

Teams who prioritize documentation and knowledge sharing have 71% quicker issue resolution times, according to an Atlassian survey. Knowledge transfer is facilitated, team changes are minimized, and overall development productivity is increased through effective documentation KPIs.

Engineering Task Planning and Predictability Careful execution

Software that works well is the result of careful preparation. Analyze the division of work, predictability, and WIP count—prudent task segmentation results in a well-structured project. Predictability measures commitment fulfillment and provides information for ongoing development. To speed up the development process and foster an efficient, focused development journey, strive for optimum WIP management.

According to Project Management Institute (PMI) research, 89% of projects are completed under budget and on schedule by high-performing firms. Predictability and WIP count are task planning KPIs that provide unambiguous execution routes, effective resource allocation, and on-time completion, all contributing to project success.

Putting these KPIs into Action

Implementing these key performance indicators is important for aligning developers' efforts with strategic objectives and improving the software delivery process.

Identify Strategic Objectives

Understand the strategic goals of your organization or project. It can include purposes related to product quality, time to market, customer satisfaction, or revenue growth.

Select relevant KPIs

Choose KPIs that are directly aligned with your strategic goals. Such as for code quality: code coverage or defect density can be the right KPI. For team health and adaptability, consider metrics like sprint burndown or change failure rate.

Regular Monitoring and Analysis

Track progress by continuously monitoring software engineering KPIs such as sprint burndown and team velocity. Regularly analyze the data to identify trends, patterns, and blind spots.

Communication and Transparency

Share KPIs results and progress with your development team. Transparency results in accountability. Hence, ensuring everyone is aligned with the business objectives as well as aware of the goal setting.

Strategic KPIs for Software Excellence Navigation

These 9 KPIs are essential for software development. They give insight into every aspect of the process and help teams grow strategically, amplify quality, and innovate for the user. Remember that each indicator has significance beyond just numbers. With these KPIs, you can guide your team towards progress and overcome obstacles. You have the compass of software expertise at your disposal.

By successfully incorporating these KPIs into your software development process, you may build a strong foundation for improving code quality, increasing efficiency, and coordinating your team's efforts with overall business objectives. These strategic indicators remain constant while the software landscape changes, exposing your route to long-term success.

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Top 10 Agile Metrics and Why they Matter?

Agile has transformed the way companies work. It reduces the time to deliver value to end-users and lowers the cost. In other words, Agile methodology helps ramp up the developers teams’ efficiency.

But to get the full benefits of agile methodology, teams need to rely on agile metrics. They are realistic and get you a data-based overview of progress. They help in measuring the success of the team.

Let’s dive deeper into Agile metrics and a few of the best-known metrics for your team:

What are Agile Metrics?

Agile metrics can also be called Agile KPIs. These are the metrics that you use to measure the work of your team across SDLC phases. It helps identify the process's strengths and expose issues, if any, in the early stages.Besides this, Agile metrics help cover different aspects including productivity, quality, and team health.

A few benefits of Agile metrics are:

  • It fosters continuous improvement for the team.
  • It helps in identifying team challenges and tracks progress toward your goals.
  • It keeps a pulse on agile development.
  • It fastens up delivery time for products to end-users.
  • It helps in avoiding guesswork about bandwidth.

Importance of Agile Metrics

Increase Productivity

With the help of agile project metrics, development teams can identify areas for improvement, track progress, and make informed decisions. This enhances efficiency which further increases team productivity.

Build Accountability and Transparency

Agile performance metrics provide quantifiable data on various aspects of work. This creates a shared understanding among team members, stakeholders, and leadership. Hence, contributing to a more accountable and transparent development environment.

Foster Continuous Improvement in the Team

These meaningful metrics provide valuable insights into various aspects of the team's performance, processes, and outcomes. This makes it easy to assess progress and address blind spots. Therefore, fostering a culture that values learning, adaption, and ongoing improvement.

Speed Up Product Delivery Time

Agile metrics including burndown chart, escaped defect rate, and cycle time provide software development teams with data necessary to optimize the development process and streamline workflow. This enables teams to prioritize effectively. Hence, ensuring delivered features meet user needs and improve customer satisfaction.

Wanna Setup Agile Metrics for your Team?

Types of Agile Metrics

Kanban Metrics

This metric focuses on workflow, organizing and prioritizing work, and the amount of time invested to obtain results. It uses visual cues for tracking progress over time.

Scrum Metrics

Scrum metrics focus on the predictable delivery of working software to customers. It analyzes sprint effectiveness and highlights the amount of work completed during a given sprint.

Lean Metrics

This metric focuses on productivity and quality of work output, flow efficiency, and eliminating wasteful activities. It helps in identifying blind spots and tracking progress toward lean goals.

Top 10 Agile metrics

Below are a few powerful agile metrics you should know about:

Lead Time

Lead time metric measures the total time elapsed from the initial request being made till the final product is delivered. In other words, it measures the entire agile system from start to end. The lower the lead time, the more efficient the entire development pipeline is.

Lead time helps keep the backlog lean and clean. This metric removes any guesswork and predicts when it will start generating value. Besides this, it helps in developing a business requirement and fixing bugs.

Cycle Time

This popular metric measures how long it takes to complete tasks. Less cycle time ensures more tasks are completed. When the cycle time exceeds a sprint, it signifies that the team is not completing work as it is supposed to. This metric is a subset of lead time.

Moreover, cycle time focuses on individual tasks. Hence, a good indicator of the team’s performance and raises red flags, if any in the early stages.

Cycle time makes project management much easier and helps in detecting issues when they arise.

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Velocity

This agile metric indicates the average amount of work completed in a given time, typically a sprint. It can be measured with hours or story points. As it is a result metric, it helps measure the value delivered to customers in a series of sprints. Velocity predicts future milestones and helps in estimating a realistic rate of progress.

The higher the team’s velocity, the more efficient teams are at developing processes.

Although, the downside of this metric is that it can be easily manipulated by teams when they have to satisfy velocity goals.

Sprint Burndown

The sprint burndown chart helps in knowing how many story points have been completed and are remaining during the sprint. The output is measured in terms of hours, story points, or backlogs which allows you to assess your performance against the set parameters. As Sprint is time-bound, it is important to measure it frequently.

The most common ones include time (X-axis) and task (Y-axis).Sprint Burndown aims to get all forecasted work completed by the end of the sprint.

What is Burndown Chart in Scrum?

Work in Progress

This metric demonstrates how many work items you currently have ‘in progress’ in your working process. It is an important metric that helps keep the team focused and ensures a continuous work flow. Unfinished work can result in sunk costs.

An increase in work in progress implies that the team is overcommitted and not using their time efficiently. Whereas, the decrease in work in progress states that the work is flowing through the system quickly and the team can complete tasks with few blockers.

Moreover, limited work in progress also has a positive effect on cycle time.

Throughput

This is another agile metric that measures the number of tasks delivered per sprint. It can also be known as measuring story points per iteration. It represents the team’s productivity level. Throughput can be measured quarterly, monthly, weekly, per release, per iteration, and in many other ways.

It allows you in checking the consistency level of the team and identify how much software can be completed within a given period. Besides this, it can also help in understanding the effect of workflow on business performance.

But, the drawback of this metric is that it doesn’t show the starting points of tasks.

Code Coverage

This agile metric tracks the coding process and measures how much of the source code is tested. It helps in giving a good perspective on the quality of the product and reflects the raw percentage of code coverage. It is measured by a number of methods, statements, conditions, and branches that comprise your unit testing suite.

When the code coverage is lower, it implies that the code hasn’t been thoroughly tested. It can further result in low quality and a high risk of errors. But, the downside of this metric is that it excludes other types of testing. Hence, higher code statistics may not always imply excellent quality.

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Escape Defects

This key metric reveals the quality of the products delivered and identifies the number of bugs discovered after the release enters production. Escape defects include changes, edits, and unfixed bugs.

It is a critical metric as it helps in identifying the loopholes and technical debt in the process. Hence, improving the production process.

Ideally, escape defects should be minimized to zero. As if the bugs are detected after release, it can result in cause immense damage to the product.

Cumulative Flow Diagram

Cumulative flow diagram visualizes the team’s entire workflow. Color coding helps in showing the status of the tasks and quickly identify the obstacles in agile processes. For example, grey color represents the agile project scope, green shows completed tasks and other colored items represent the particular status of the tasks.

X-axis represents the time frame while Y-axis includes several tasks within the project.

This key metric help find bottlenecks and address them by making adjustments and improving the workflow.

Happiness Metric

One of the most overlooked metrics is the Happiness metric. It indicates how the team feels about their work. The happiness metric evaluates the team’s satisfaction and morale through a ranking on a scale. It is usually done through direct interviews or team surveys.The outcome helps in knowing whether the current work environment, team culture, and tools are satisfactory. It also lets you identify areas of improvement in practices and processes.

When the happiness metric is low yet other metrics show a positive result, it probably means that the team is burned out. It can negatively impact their morale and productivity in the long run.

Conclusion

We have mentioned the optimal well-known agile metrics. But, it is up to you which metrics you choose that can be relevant for your team and the requirements of end-users.

You can start with a single metric and add a few more. These metrics will not only help you see results tangibly but also let you take note of your team’s productivity.

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The Impact of Coding Time and How to Reduce It

The ticking clock of coding time is often considered a factor in making or breaking the success of a development project. When developers manage it well, teams can meet deadlines, deliver high-quality software, and foster collaboration.

However, sometimes coding times are high. This can cause many internal issues and affect the product development cycle.

This blog will address why coding time is high sometimes and how you can improve it.

What is Coding Time?

Coding time is the time it takes from the first commit to a branch to the eventual submission of a pull request. It is a crucial part of the development process where developers write and refine their code based on the project requirements.

What is the Importance of Coding Time?

High coding time can lead to prolonged development cycles, affecting delivery timelines. Coding time is crucial in the software development lifecycle as it can directly impact the cycle time.

Thus, managing the coding time efficiently to ensure the code completion is done on time with quicker feedback loops and a frictionless development process is essential.

What is the Impact of Coding Time?

Maintaining the right coding time has several benefits for engineering teams.

Projects Progress Faster

When you reduce the coding time, developers can complete more tasks. This moves the project faster and results in shorter development cycles.

Efficient Collaboration

With less time spent on coding, developers can have enough time for collaborative activities such as code reviews. These are crucial for a team to function well and enable knowledge sharing.

Higher Quality

When coding time is lesser, developers can focus more on quality by conducting testing and debugging processes. This results in cleaner code.

What Factors affect Coding Time?

While less coding time has several benefits, this often isn’t the reality. However, high coding time is not just the result of a team member being lazy – several reasons cause high coding time.

Complex Tasks

Whenever the tasks or features are complicated, additional coding time is needed compared to the more straightforward tasks.

Developers also try to complete the entire tasks in one go which can be hard to achieve. This leads to the developer getting overwhelmed and, eventually, prolonging the coding time. Code review plays a vital role in this context, allowing for constructive feedback and ensuring the quality of the codebase.For software developers, breaking down work into smaller, more manageable chunks is crucial to make progress and stay focused. It’s important to commit small changes frequently to move forward quickly and receive feedback more often. This ensures that the development process runs smoothly and stays on track.

Requirement Clarity

When the requirement is defined poorly, developers will struggle to be efficient. It leads to higher coding time in understanding the requirement, seeking clarification, and making assumptions based on this.

It is essential to establish clear and comprehensive requirements before starting any coding work. This helps developers create an accurate roadmap, pave the way for smoother debugging processes, and reduce the chances of encountering unexpected obstacles. Effective planning and scoping improve the efficiency of the coding process, resulting in timely and satisfactory outcomes.

Varied Levels of Skill and Experience

In a team, there will be developers with different skillset and experience. Additionally, the expertise and familiarity of the developers with the codebase and the technology stack can affect their coding speed.

Maintaining Focus and Consistency

Maintaining focus and staying on-task while coding is crucial for efficient development. Task languishing is a common issue that can arise due to distractions or shifting priorities, leading to abandoned tasks and decreased productivity.

A survey showed that developers spent only one-third of their time writing new code but spent 35% managing code with code maintenance, testing, and solving security issues.

To avoid this, it’s essential to conduct regular progress reviews. Teams must implement a systematic review process to identify potential issues and address them promptly by reallocating resources as needed. Consistency and focus throughout the development cycle are key for optimizing coding time.

High-Risk

When a developer has too many ongoing projects, they are forced to frequently multitask and switch contexts. This can lead to a reduction in the amount of time they spend working on a particular branch or issue, resulting in an increase in their coding time metric.Use the worklog to understand the dev’s commits over a timeline to different issues. If a developer makes sporadic contributions to various issues, it may be indicative of frequent context switching during a sprint. To mitigate this issue, it is advisable to balance and rebalance the assignment of issues evenly and encourage the team to avoid multitasking by focusing on one task at a time. This approach can help reduce coding time.

How Can You Prevent High Code Time?

Setting Up Slack Alerts for High-Risk Work

Set goals for the work at risk where the rule of thumb is keeping the PR with less than 100 code changes and refactor size as above 50%.To achieve the team goal of reducing coding time, real-time Slack alerts can be utilised to notify the team of work at risk when large and heavily revised PRs are published. By using these alerts, it is possible to identify and address issues, story-points, or branches that are too extensive in scope and require breaking down.

Empowering Code Review Efficiency

Ensuring fast and efficient code reviews is crucial to optimize coding time. It’s important to inform developers of how timely reviews can speed up the entire development process.

To accomplish this, code review automation tools should be used to improve the review process. These tools can separate small reviews from large ones and automatically assign them to available developers. Furthermore, scheduling specialist reviews can guarantee that complex tasks receive the necessary attention without causing any undue delays.

Embracing Data-Driven Development

Improving coding productivity necessitates the adoption of data-driven practices. Teams should incorporate code quality tools that can efficiently monitor coding time and project advancement.

Such tools facilitate the swift identification of areas that require attention, enabling developers to refine their methods continuously. Using data-driven insights is the key to developing more effective coding practices.

Prioritize Task Clarity

Before starting the coding process, thoroughly defining and clarifying the project requirements is extremely important. This crucial step guarantees that developers have a complete comprehension of what needs to be achieved, ultimately resulting in a successful outcome.

Pair Programming

Pair programming involves two developers working together on the same code at the same time. This can help reduce coding time by allowing developers to collaborate and share ideas, which can lead to faster problem-solving and more efficient coding. Incorporating the code review process into the pair programming process also ensures the quality of the codebase.

Encourage Collaboration

Encouraging open communication and collaboration among team members is crucial to creating a productive and positive work environment. This fosters a culture of teamwork and enables efficient problem-solving through shared ideas. Working together leads to more significant achievements than individuals can accomplish alone.4. Automate Repetitive Processes: Utilize automation tools to streamline repetitive coding tasks, such as code generation or testing, to save time and effort.

Continuous Learning and Skill Development

Developers must always stay up to date with the latest technologies and best practices. This is crucial for increasing coding speed and efficiency while enhancing the quality of the code. Continuous learning and skill development are essential to maintain a competitive edge in the industry.

Balance Workload in the Team

To manage workloads and assignments effectively, it is recommended to develop a habit of regularly reviewing the Insights tab, and identifying long PRs on a weekly or even daily basis. Additionally, examining each team member’s workload can provide valuable insights. By using this data collaboratively with the team, it becomes possible to allocate resources more effectively and manage workloads more efficiently.

Use a Framework

Using a framework, such as React or Angular, can help reduce coding time by providing pre-built components and libraries that can be easily integrated into the application.

Rapid Prototyping

Rapid prototyping involves creating a quick and simple version of the application to test its functionality and usability. This can help reduce coding time by allowing developers to quickly identify and address any issues with the application.

Use Agile Methodologies

Agile methodologies, such as Scrum and Kanban, emphasize continuous delivery and feedback, which can help reduce coding time by allowing developers to focus on delivering small, incremental improvements to the application.

Code Reuse

Reusing code that has already been written can help reduce coding time by eliminating the need to write code from scratch. This can be achieved by using code libraries, modules, and templates.

Leverage AI Tools

Incorporating artificial intelligence tools can enhance productivity by automating code review and repetitive tasks, minimizing coding errors, and accelerating the overall development cycle. These AI tools use various techniques including neural networks and machine learning algorithms to generate new content.

How Typo Helps in Identifying High Coding Time?

Typo provides instantaneous cycle time measurement for both the organization and each development team using their Git provider.

Our methodology divides cycle time into four phases:

  • The coding time is calculated from the initial commit to the creation of a pull request or merge request.
  • The pickup time is measured from the PR creation to the beginning of the review. 
  • Review time is calculated from the start of the review to when the code is merged, and 
  • Merge time is measured from when the code is merged to when it is released.

When the coding time is high, your main dashboard will display the coding time as red.

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Identify delay in the ‘Insights’ section at the team level and sort the teams by the cycle time. Further, click on the team to deep dive into cycle time breakdown of each team and see the delays in the coding time.

Make Development Processes Better by Reducing Coding Time

Coding times are the cornerstones of efficient software development. Thus, when its impact on project timelines is recognized, engineering teams can imbibe best practices and preventative strategies to deliver quality code on time.

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Why prefer PR Cycle Time as a Metric over Velocity?

PR cycle time and velocity are two commonly used metrics for measuring the efficiency and effectiveness of software development teams. These metrics help estimate how long your teams can complete a piece of work.

But, among these two, PR cycle time is often prioritized and preferred over velocity.

Therefore, in this blog, we understand the difference between these two metrics. Further, we will dive into the reason behind the PR cycle time over the velocity metric.

What is the PR Cycle Time?

PR cycle time measures the process efficiency. In other words, it is the measurement of how much time it takes for your team to complete individual tasks from start to finish. It let them identify bottlenecks in the software development process and implement changes accordingly. Hence, allowing development work to flow smoother and faster through the delivery process.

Benefits of PR Cycle Time

Assess Efficiency

PR Cycle time lets team members understand how efficiently they are working. A shorter PR cycle time means developers are spending less time waiting for code reviews and integration of code. Hence, indicates a high level of efficiency.

Faster Time-to-Market

A shorter PR Cycle time means that features or updates can be released to end-users sooner. As a result, it helps them to stay competitive and meet customer demands.

Improves Agility

Short PR Cycle time is a key component of agile software development. Hence, allows team members to adapt to changing requirements more easily.

What is Velocity?

Velocity is the measurement of team efficiency. It estimates how many story points an agile team can complete within a sprint. This metric is usually measured in weeks. As a result, it helps developer teams to plan and decide how much work to include in future sprints. But, the downside is that it doesn’t consider the quality of work or the time it takes to complete individual tasks.

Benefits of Velocity

Effective Resource Allocation

By understanding development velocity, engineering managers and stakeholders can allocate resources more effectively. Hence, it ensures that development teams are neither overburdened or underutilized.

Improves Collaboration and Team Morale

When velocity improves, it gives team members a sense of satisfaction from constantly delivering high-quality products. Hence, it improves their morale and allows them to collaborate with each other effectively.

Identify Bottlenecks

A decline in velocity metrics signifies potential issues within the development process which includes team conflicts or technical debt. Hence, it allows us to address the issues early to maintain productivity.

PR Cycle Time over Velocity: Know the ‘Why’ Behind it

PR Cycle Time Cannot be Easily Manipulated

PR cycle time is a more objective unit of measurement compared to story points in the production process. While many organizations use story points to estimate time-bound work since it is subjective. Hence, it is easy to manipulate. To increase velocity, you have to overestimate how long it will take to complete the task and therefore, add a larger number to your issue tracker.

Although PR cycle time may also be manipulated, it is most likely to work in your favor. By this, lowering the cycle time allows you to complete the work measurably faster. This further allows you to identify and fix blind spots quickly.

As a result, PR cycle time is a more challenging and tangible goal.

PR Cycle Time Helps in Predictability and Planning

PR cycle time, an essential component of continuous improvement, improves your team’s ability to plan and estimate work. It gives you an accurate picture of how long it will take to move through the development process. Hence, offering real-time visibility and insights into a developer’s task. This further allows you to predict and forecast future work. In case, if the issue goes on longer than expected, you can discuss it with your team on a prior basis.

In the case of velocity, it cannot help in figuring out the why behind the work that took longer than expected. Hence, further planning and predicting the work accordingly wouldn’t be possible in this case.

PR Cycle Time Helps in Identifying Outliers

Outliers are the units of work that take significantly longer than the average. PR cycle time metric is more reliable than the velocity in spotting outliers and anomalies in software development. It is because it measures the time it takes to complete a single unit of work. Therefore, PR cycle time helps in knowing the specific causes of delays in work.

Moreover, it also helps in getting granular insights into the development process. Hence, allowing your engineering team to improve their performance.

PR Cycle Time is Directly Connected to the Business Results

Among velocity and PR cycle time metrics, only the latter is directly related to business outcomes. It is a useful metric that determines how fast you can ship value to your customers; allowing you to improve speed and their planning accurately.

Moreover, cycle time is a great metric for continuously improving your team’s ability to iterate quickly. As it can help in spotting bottlenecks, inefficiencies, and areas of improvement in their processes.

How Typo measure PR Cycle Time?

Measuring cycle time using Jira or other project management tools is a manual and time-consuming process, which requires reliable data hygiene to deliver accurate results. Unfortunately, most engineering leaders have insufficient visibility and understanding of their teams’ cycle time.

Typo provides instantaneous cycle time measurement for both your organization and each development team using your Git provider.

Our methodology divides cycle time into four phases:

  • The coding time is calculated from the initial commit to the creation of a pull request or merge request.
  • The pickup time is measured from the PR creation to the beginning of the review.
  • Review time is calculated from the start of the review to when the code is merged, and
  • Merge time is measured from when the code is merged to when it is released.

The subsequent phase involves analyzing the various aspects of your cycle time, including the organizational, team, iteration, and even branch levels. For instance, if an iteration has an average review time of 47 hours, you will need to identify the branches that are taking longer than usual and work with your team to address the reasons for the delay.

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But, Does it Mean Only PR Cycle Time is to be Considered?

PR cycle time shouldn’t be the sole metric to measure software development productivity. If you do so, it would mean compromising other aspects of the software development product. Hence, you can balance it with other metrics such as DORA metrics (Deployment frequency, Lead time for change, Change failure rate and Time to restore service) too.

You can familiarize yourself with the SPACE framework when thinking about metrics to adopt in your organization. It is a research-based framework that combines quantitative and qualitative aspects of the developer and the surroundings to give a holistic view of the software development process.

At Typo, we consider the above-mentioned metrics to measure the efficiency and effectiveness of software engineering teams. Through these metrics, you can gain real-time visibility into SDLC metrics, identify bottlenecks and drive continuous improvements.

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The Ultimate DORA DevOps Guide: Boost Your Dev Efficiency with DORA Metrics

Imagine having a powerful tool that measures your software team’s efficiency, identifies areas for improvement, and unlocks the secrets to achieving speed and stability in software development – that tool is DORA metrics.

DORA metrics offer valuable insights into the effectiveness and productivity of your team. By implementing these metrics, you can enhance your dev practices and improve outcomes.

In this blog, we will delve into the importance of DORA metrics for your team and explore how they can positively impact your software team’s processes. Join us as we navigate the significance of these metrics and uncover their potential to drive success in your team’s endeavors.

What are DORA Metrics?

DevOps Research and Assessment (DORA) metrics are a compass for engineering teams striving to optimize their development and operations processes.

In 2015, The DORA team was founded by Gene Kim, Jez Humble, and Dr. Nicole Forsgren to evaluate and improve software development practices. The aim is to enhance the understanding of how development teams can deliver software faster, more reliably, and of higher quality.

Software teams use DORA DevOps metrics in an organization to help improve their efficiency and, as a result, enhance the effectiveness of company deliverables. It is the industry standard for evaluating dev teams and allows them to scale.

The key DORA metrics include deployment frequency, lead time for changes, mean time to recovery, and change failure rate. They have been identified after six years of research and surveys by the DORA team.

To achieve success with DORA metrics, it is crucial to understand them and learn the importance of each metric. Here are the four key DORA metrics:

Implementing DORA Metrics to Improve Dev Performance & Productivity?

Deployment Frequency: Boosting Agility

Organizations need to prioritize code deployment frequency to achieve success and deliver value to end users. However, it’s worth noting that what constitutes a successful deployment frequency may vary from organization to organization.

Teams that underperform may only deploy monthly or once every few months, whereas high-performing teams deploy more frequently. It’s crucial to continuously develop and improve to ensure faster delivery and consistent feedback. If a team needs to catch up, implementing more automated processes to test and validate new code can help reduce recovery time from errors.

Why is Deployment Frequency Important?

  • Continuous delivery enables faster software changes and quicker response to market demands.
  • Frequent deployments provide valuable user feedback for improving software efficiently.
  • Deploy smaller releases frequently to minimize risk. This approach reduces the impact of potential failures and makes it easier to isolate issues. Taking small steps ensures better control and avoids risking everything.
  • Frequent deployments support agile development by enabling quick adaptation to market changes and facilitating continuous learning for faster innovation.
  • Frequent deployments promote collaboration between teams, leading to better outcomes and more successful projects. 

Use Case:

In a dynamic market, agility is paramount. Deployment Frequency measures how frequently code is deployed. Infrequent deployments can cause you to lag behind competitors. Increasing Deployment Frequency facilitates more frequent rollouts, hence, meeting customer demands effectively.

Lead Time for Changes: Streamline Development

This metric measures the time it takes to implement changes and deploy them to production directly impacts their experience, and this is the lead time for changes.

If we notice longer lead times, which can take weeks, it may indicate that you need to improve the development or deployment pipeline. However, if you can achieve lead times of around 15 minutes, you can be sure of an efficient process. It’s essential to monitor delivery cycles closely and continuously work towards streamlining the process to deliver the best experience for customers.

Why is the Lead Time for Changes Important? 

  • Short lead times in software development are crucial for success in today’s business environment. By delivering changes rapidly, organizations can seize new opportunities, stay ahead of competitors, and generate more revenue.
  • Short lead times help organizations gather feedback and validate assumptions quickly, leading to informed decision-making and aligning software development with customer needs. Being customer-centric is critical for success in today’s competitive world, and feedback loops play a vital role in achieving this.
  • By reducing lead time, organizations gain agility and adaptability, allowing them to swiftly respond to market changes, embrace new technologies, and meet evolving business needs.
  • Shorter lead times enable experimentation, learning, and continuous improvement, empowering organizations to stay competitive in dynamic environments.
  • Reducing lead time demands collaborative teamwork, breaking silos, fostering shared ownership, and improving communication, coordination, and efficiency. 

Use Case:

Picture your software development team tasked with a critical security patch. Measuring Lead Time for Changes helps pinpoint the duration from code commit to deployment. If it goes for a long run, bottlenecks in your CI/CD pipeline or testing processes might surface. Streamlining these areas ensures rapid responses to urgent tasks.

Change Failure Rate: Ensuring Stability

The change failure rate measures the code quality released to production during software deployments. Achieving a lower failure rate than 0-15% for high-performing DevOps teams is a compelling goal that drives continuous improvement in skills and processes. Establishing failure boundaries tailored to your organization’s needs and committing to reducing the failure rate is essential. By doing so, you enhance your software solutions and deliver exceptional user experiences.

Why is Change Failure Rate Important? 

  • It enhances user experience and builds trust by reducing failures; we elevate satisfaction and cultivate lasting positive relationships.
  • It protects your business from financial risks, and you avoid revenue loss, customer churn, and brand damage by reducing failures.
  • Reduce change failures to allocate resources effectively and focus on delivering new features.

Use Case:

Stability is pivotal in software deployment. The change Failure Rate measures the percentage of changes that fail. A high failure rate could signify inadequate testing or insufficient quality control. Enhancing testing protocols, refining code reviews, and ensuring thorough documentation can reduce the failure rate, enhancing overall stability.

Mean Time to Recover (MTTR): Minimizing Downtime

Mean Time to Recover (MTTR) measures the time to recover a system or service after an incident or failure in production. It evaluates the efficiency of incident response and recovery processes. Optimizing MTTR aims to minimize downtime by resolving incidents through production changes. The goal is to build robust systems that can detect, diagnose, and rectify problems. Organizations ensure minimal disruption and work towards continuous improvement in incident resolution.

Why is Mean Time to Recover Important?

  • Minimizing MTTR enhances user satisfaction by reducing downtime and resolution times.
  • Reducing MTTR mitigates the negative impacts of downtime on business operations, including financial losses, missed opportunities, and reputational damage.
  • Helps meet service level agreements (SLAs) that are vital for upholding client trust and fulfilling contractual commitments.

Use Case:

Downtime can be detrimental, impacting revenue and customer trust. MTTR measures the time taken to recover from a failure. A high MTTR indicates inefficiencies in issue identification and resolution. Investing in automation, refining monitoring systems, and bolstering incident response protocols minimizes downtime, ensuring uninterrupted services.

Key Use Cases

Development Cycle Efficiency

Metrics: Lead Time for Changes and Deployment Frequency

High Deployment Frequency, Swift Lead Time:

Teams with rapid deployment frequency and short lead time exhibit agile development practices. These efficient processes lead to quick feature releases and bug fixes, ensuring dynamic software development aligned with market demands and ultimately enhancing customer satisfaction.

Low Deployment Frequency despite Swift Lead Time:

A short lead time coupled with infrequent deployments signals potential bottlenecks. Identifying these bottlenecks is vital. Streamlining deployment processes in line with development speed is essential for a software development process.

Code Review Excellence

Metrics: Comments per PR and Change Failure Rate

Few Comments per PR, Low Change Failure Rate:

Low comments and minimal deployment failures signify high-quality initial code submissions. This scenario highlights exceptional collaboration and communication within the team, resulting in stable deployments and satisfied end-users.

Abundant Comments per PR, Minimal Change Failure Rate:

Teams with numerous comments per PR and a few deployment issues showcase meticulous review processes. Investigating these instances ensures review comments align with deployment stability concerns, ensuring constructive feedback leads to refined code.

Developer Responsiveness

Metrics: Commits after PR Review and Deployment Frequency

Frequent Commits after PR Review, High Deployment Frequency:

Rapid post-review commits and a high deployment frequency reflect agile responsiveness to feedback. This iterative approach, driven by quick feedback incorporation, yields reliable releases, fostering customer trust and satisfaction.

Sparse Commits after PR Review, High Deployment Frequency:

Despite few post-review commits, high deployment frequency signals comprehensive pre-submission feedback integration. Emphasizing thorough code reviews assures stable deployments, showcasing the team’s commitment to quality.

Quality Deployments

Metrics: Change Failure Rate and Mean Time to Recovery (MTTR)

Low Change Failure Rate, Swift MTTR:

Low deployment failures and a short recovery time exemplify quality deployments and efficient incident response. Robust testing and a prepared incident response strategy minimize downtime, ensuring high-quality releases and exceptional user experiences.

High Change Failure Rate, Rapid MTTR:

A high failure rate alongside swift recovery signifies a team adept at identifying and rectifying deployment issues promptly. Rapid responses minimize impact, allowing quick recovery and valuable learning from failures, strengthening the team’s resilience.

Code Collaboration Efficiency

Metrics: Comments per PR and Commits after PR is Raised for Review

In collaborative software development, optimizing code collaboration efficiency is paramount. By analyzing Comments per PR (reflecting review depth) alongside Commits after PR is Raised for Review, teams gain crucial insights into their code review processes.

High Comments per PR, Low Post-Review Commits:

Thorough reviews with limited code revisions post-feedback indicate a need for iterative development. Encouraging developers to iterate fosters a culture of continuous improvement, driving efficiency and learning.

Low Comments per PR, High Post-Review Commits:

Few comments during reviews paired with significant post-review commits highlight the necessity for robust initial reviews. Proactive engagement during the initial phase reduces revisions later, expediting the development cycle.

Impact of PR Size on Deployment

Metrics: Large PR Size and Deployment Frequency

The size of pull requests (PRs) profoundly influences deployment timelines. Correlating Large PR Size with Deployment Frequency enables teams to gauge the effect of extensive code changes on release cycles.

High Deployment Frequency despite Large PR Size:

Maintaining a high deployment frequency with substantial PRs underscores effective testing and automation. Acknowledge this efficiency while monitoring potential code intricacies, ensuring stability amid complexity.

Low Deployment Frequency with Large PR Size:

Infrequent deployments with large PRs might signal challenges in testing or review processes. Dividing large tasks into manageable portions accelerates deployments, addressing potential bottlenecks effectively.

PR Size and Code Quality:

Metrics: Large PR Size and Change Failure Rate

PR size significantly influences code quality and stability. Analyzing Large PR Size alongside Change Failure Rate allows engineering leaders to assess the link between PR complexity and deployment stability.

High Change Failure Rate with Large PR Size:

Frequent deployment failures with extensive PRs indicate the need for rigorous testing and validation. Encourage breaking down large changes into testable units, bolstering stability and confidence in deployments.

Low Change Failure Rate despite Large PR Size:

A minimal failure rate with substantial PRs signifies robust testing practices. Focus on clear team communication to ensure everyone comprehends the implications of significant code changes, sustaining a stable development environment. Leveraging these correlations empowers engineering teams to make informed, data-driven decisions — a great way to drive business outcomes— optimizing workflows, and boosting overall efficiency. These insights chart a course for continuous improvement, nurturing a culture of collaboration, quality, and agility in software development endeavors.

Help your Team with DORA Metrics!

In the ever-evolving world of software development, harnessing the power of DORA DevOps metrics is a game-changer. By leveraging DORA key metrics, your software teams can achieve remarkable results. These metrics are an effective way to enhance customer satisfaction, mitigate financial risks, meet service-level agreements, and deliver high-quality software.

Implementing DORA Metrics to Improve Dev Performance & Productivity?

Featured Comments

Profile photo of Gaurav Batra
Gaurav Batra, CTO & Cofounder @ Semaai

“This article is an amazing eye-opener for many engineering leaders on how to use DORA metrics. Correlating metrics gives the real value in terms of SDLC insights and that's what is the need of the hour."

Marian Kamenistak, Engineering Leadership Coach
Marian Kamenistak, Engineering Leadership Coach

“That is the ultimate goal - connecting DevOps to DORA. Super helpful article for teams looking at implementing DORA.”

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Deconstructing Cycle Time in Software Development

Numerous metrics are available for monitoring software development progress and generating reports that indicate the performance of your engineering team can be a time-consuming task, taking several hours or even days.Through our own research and collaboration with industry experts like DORA, we suggest concentrating on cycle time, also referred to as a lead time for changes, which we consider the most crucial metric to monitor.This measurement indicates the performance and efficiency of your teams and developers. In this piece, we will cover what cycle time entails, its significance, methods for calculating it, and actions to enhance it.

What is Cycle Time?

Cycle Time in software development denotes the duration between an engineer’s first commit and code deployment, which some teams also refer to as lead time. This measurement indicates the time taken to finalize a specific development task. Cycle time serves as a valuable metric for deducing a development team’s process speed, productivity, and capability of delivering functional software within a defined time frame.

Leaders who measure cycle time gain insight into the speed of each team, the time taken to finish specific projects, and the overall performance of teams relative to each other and the organization. Moreover, optimizing cycle time enhances team culture and stimulates innovation and creativity in engineering teams.

However, cycle time is a lagging indicator, implying that it confirms ongoing patterns rather than measures productivity. As such, it can be utilized as a signal of underlying problems within a team.

Since cycle time reflects the speed of team performance, most teams aim to maintain low cycle times that enhance their efficiency. According to the Accelerate State of DevOps Report research, the top 25% of successful engineering teams achieve a cycle time of 1.8 days, while the industry-wide median cycle time is 3.4 days. On the other hand, the bottom 25% of teams have a cycle time of 6.2 days.

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How to Measure Cycle Time?

Measuring cycle time using Jira or other project management tools is a manual and time-consuming process, which requires reliable data hygiene to deliver accurate results. Unfortunately, most engineering leaders have insufficient visibility and understanding of their teams' cycle time.Typo provides instantaneous cycle time measurement for both your organization and each development team using your Git provider.Our methodology divides cycle time into four phases:

  • The coding time is calculated from the initial commit to the creation of a pull request or merge request.
  • The pickup time is measured from the PR creation to the beginning of the review. 
  • Review time is calculated from the start of the review to when the code is merged, and 
  • Merge time is measured from when the code is merged to when it is released.

The subsequent phase involves analyzing the various aspects of your cycle time, including the organizational, team, iteration, and even branch levels. For instance, if an iteration has an average review time of 47 hours, you will need to identify the branches that are taking longer than usual and work with your team to address the reasons for the delay.

What Causes High Cycle Time?

Although managers and leaders are aware of the significance of cycle time, they aren't necessarily armed with the information necessary to understand why the cycle time of their team may be higher or lower than ideal. Leaders may make decisions that have a beneficial impact on developer satisfaction, productivity, and team performance by understanding the processes that make up cycle time and exploring its constituent parts.Cycle time could increase as engineers wait for builds to finish and tests to pass before the PR is ready for review. When engineers must make modifications following each review and wait for a drawn-out and delayed CI/CD that extends the time to merge, the process becomes even more wasteful. This not only lengthens the cycle time but also causes contributors to feel frustrated.

Large PRs

The time it takes to open a PR increases because large-sized PRs take longer to code and, as a result, stay closed for too long. For instance, the majority of teams aim for PR sizes to be under 300 changes, and as this limit rises, the time to open the PR lengthens. Even when huge PRs are opened, they are often not moved to the code review stage because most reviewers are reluctant to do so for the following two reasons:

A high PR indicates that the reviewer put a lot of effort into the review. To accommodate a significant code review, the reviewer must plan and significantly restructure their current schedule. It takes heavy and intense effort.

Huge PRs are notorious for their capacity to add a number of new bugs.

Lack of Documentation

Code comments and other forms of documentation in the code are best practices that are regrettably frequently ignored. Reviewers and future collaborators can evaluate and work on code more quickly and effectively with the use of documentation, cutting down on pickup time and rework time. Coding standards assist authors in starting off with pull requests that are in better shape. They also assist reviewers in avoiding repeated back and forth on fundamental procedures and standards. When working on code that belongs to other teams, this documentation is very useful for cross-team or cross-functional collaboration. Various teams adhere to various coding patterns, and consistency is maintained by documentation.

Teams can greatly benefit from a readme that is relevant to a codebase and contains information about coding patterns, and supporting materials, such as how and where to add logs, coding standards, emit metrics, approval requirements, etc.

High CI/CD time

Cycle time could increase as engineers wait for builds to finish and tests to pass before the PR is ready for code review. When engineers must make modifications following each review and wait for a drawn-out and delayed CI/CD that extends the time to merge, the process becomes even more wasteful. This not only lengthens the cycle time but also causes contributors to feel frustrated. Moreover, when the developers don't adhere to coding standards before entering the CI/CD pipeline can increase cycle time and reduce code quality.

Developers' Burnout

Engineers may struggle with numerous WIP PRs due to an unmanaged and heavy workload, in turn reporting lengthier coding and rework times. Reviewers are more likely to become overburdened by the sheer volume of review requests at the end of a sprint than by a steady stream of PRs. This limits reviewers’ own coding time as their coding skills start deteriorating and causes a large number of PRs to be merged without review, endangering the quality of the code.

The team experiences a high cycle time as reviewers struggle to finish their own code, the reviews, and the rework, and they suffer burnout.

Lack of Sanity Checks

When teams fail to perform simple sanity checks and debugging needs before creating PRs (such as linting, test code coverage, and initial debugging), it results in avoidable nitpicks during a code review (where the reviewer may be required to spend time pointing out formatting errors or test coverage thresholds that the author should have covered by default).

How Optimizing Cycle Time Helps Engineering Leaders?

So, now that you're confidently tracking cycle time and all four phases, what can you do to make your engineering organization's cycle time more consistent and efficient? How can you reap the benefits of good developer experience, efficiency, predictability, and keeping your business promises?

Benchmark Your cycle Time & Identify Problem Areas

Start measuring the cycle time and breakdown in four phases in real-time. Start comparing the benchmarks with the industry standards.

Once you’ve benchmarked your cycle time and all four phases, you’ll know which areas are causing bottlenecks and require attention. Then everyone in your organisation will be on the same page about how to effectively reduce cycle time.

Set Team Goals for Each Sprint to Improve

We recommend that you focus on one or two bottlenecks at a time—for example, PR size and review time—and design your improvement strategy around them.

Bring past performance data to your next retro to help align the team. Using engineering benchmarks, provide context into performance. Then, over the next 2-3 iterations, set goals to improve one tier.

We also recommend that you develop a cadence for tracking progress. You could, for example, repurpose an existing ceremony or make time specifically for goals. ​

Automate Alerts Using Communication Tools Like Slack

Build an alert system to reduce the cycle time by utilizing Slack to assist developers in navigating a growing PR queue.

These pieces of data enable the developer to make more informed decisions. They respond to questions such as: Do I have enough time for this review during my next small break, or should I queue it?

Adopt Agile Practices

Many organizations are adopting agile methodologies. As they help in prioritizing continuous feedback, iterative development, and team collaboration. By adopting these practices, the team can leverage their coding skills and divide large programming tasks into small, manageable chunks. Hence, completing them in a shorter cycle to enable faster delivery times.

Conclusion

The most successful teams are those that have mastered the entire coding-to-deployment process and can consistently provide new value to customers.Measuring your development workflow with typo’s Engineering Benchmarks and automating improvement with Team Goals and our Slack alerts will enable your team to build and ship features more quickly while increasing developer experience and quality.

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Why DORA metrics alone are insufficient?

Consider a world where metrics and dashboards do not exist, where your work is free from constraints and you have the freedom to explore your imagination, creativity, and innovative ideas without being tethered to anything.

It may sound like a utopian vision that anyone would crave, right? But, it is not a sentiment shared by business owners and managers. They operate in a world where OKRs, KPIs, and accountability define performance. In this environment, dreaming and fairy tales have no place.

Given that distributed teams are becoming more prevalent and the demand for rapid development is skyrocketing, managers seek ways to maintain control. Managers have started favoring “DORA metrics” to achieve this goal in development teams. By tracking and trying to enhance these metrics, managers feel as though they have some degree of authority over their engineering team’s performance and culture.

But, here’s a message for all the managers out there on behalf of developers - DORA DevOps metrics alone are insufficient and won’t provide you with the help you require.

Before we understand, why DORA is insufficient today, let’s understand what are they!

The widely used reference book for engineering leaders called Accelerate introduced the DevOps Research and Assessment (DORA) group's four metrics, known as the DORA 4 metrics.

These metrics were developed to assist engineering teams in determining two things: A) The characteristics of a top-performing team, and B) How their performance compares to the rest of the industry.

The four key DORA metrics are as follows:

Deployment Frequency

Deployment Frequency measures the frequency of code deployment to production or releases to end-users in a given time frame. It may include the code review consideration as it assesses code changes before they are integrated into a production environment.

It is a powerful driver of agility and efficiency that makes it an essential component of software development. High deployment frequency results in rapid releases without compromising the software's robustness, hence, enhancing customer satisfaction.

Lead Time for Changes

This metric measures the time between a commit being made and that commit making it to production. It helps to understand the effectiveness of the development process once coding has been initiated.

A shorter lead time signifies the DevOps teams are efficient in deploying code while a longer lead time means the testing process is obstructing the CI/CD pipeline. Hence, differentiating elite performers from low performers.

Mean Time to Recover

This metric is also known as the mean time to restore. It measures the time required to solve the incident i.e. service incident or defect impacting end-users. To lower it, the team must improve their observation skills so that failures can be detected and resolved quickly.

Minimizing MTTR enhances user satisfaction and mitigates the negative impacts of downtime on business operations.

Change Failure Rate

Change failure rate measures the proportion of deployment to production that results in degraded services. It should be kept as low as possible as it will signify successful debugging practices and thorough testing and problem-solving.

Lowering CFR is a crucial goal for any organization that wants to maintain a dependable and efficient deployment pipeline. A high change failure rate can have serious consequences, such as delays, rework, customer dissatisfaction, revenue loss, or even security breaches. 

In their words:

“Deployment Frequency and Lead Time for Changes measure velocity, while Change Failure Rate and Time to Restore Service measure stability. And by measuring these values, and continuously iterating to improve on them, a team can achieve significantly better business outcomes.”

Below are the performance metrics categorized in

  • Elite performers
  • High performers
  • Medium performers
  • Low performers

for 4 metrics –

Use Four Keys metrics like change failure rate to measure your DevOps  performance | Google Cloud Blog

What are the Challenges of DORA Metrics?

It Doesn't take into consideration all the Factors that Add to the Success of the Development Process

DORA metrics are a useful tool for tracking and comparing DevOps team performance. Unfortunately, it doesn't take into account all the factors for a successful software development process. For example, assessing coding skills across teams can be challenging due to varying levels of expertise. These metrics also overlook the actual efforts behind the scenes, such as debugging, feature development, and more.

It Doesn't Provide Full Context

While DORA metrics tell us which metric is low or high, it doesn't reveal the reason behind it. Suppose, there is an increase in lead time for changes, it could be due to various reasons. For example, DORA metrics might not reflect the effectiveness of feedback provided during code review. Hence, overlooking the true impact and value of the code review process.

The Software Development Landscape is Constantly Evolving

The software development landscape is changing rapidly. Hence, the DORA metrics may not be able to quickly adapt to emerging programming practices, coding standards, and other software trends. For instance, Code review has evolved to include not only traditional peer reviews but also practices like automated code analysis. DORA metrics may not be able to capture the new approaches fully. Hence, it may not be able to assess the effectiveness of these reviews properly.

It is Not Meant for Every Team

DORA metrics are a great tool for analyzing DevOps performance. But, It doesn't mean they are relevant to every developer's team. These key metrics work best when the deployment is done frequently, can quickly iterate on changes, and improve accordingly. For example, if your team adheres to certain coding standards or ship software monthly, it will result in low deployment frequency almost every time and helps to deliver high-quality software.

Why You’ve Been Using DORA Wrongly?

Relying solely on DORA metrics to evaluate software teams' performance has limited value. Leaders must now move beyond these metrics, identify patterns, and obtain a comprehensive understanding of all factors that impact the software development life cycle (SDLC).

For example, if a team's cycle time varies and exceeds three days, while all other metrics remain constant, managers must investigate deployment issues, the time it takes for pull requests to be approved, the review process, or a decrease in a developer's productivity.

If a developer is not coding as many days, what is the reason behind this? Is it due to technical debt, frequent switching between tasks, or some other factor that hasn't yet been identified? Therefore, leaders need to look beyond the DORA metrics and understand the underlying reasons behind any deviations or trends in performance.

Combine DORA Metrics with Other Engineering Analytics

For DORA to produce reliable results, software development teams must have a clear understanding of the metrics they are using and why they are using them. DORA can provide similar results for teams with similar deployment patterns. But, it is also essential to use the data to advance the team’s performance rather than simply relying on the numbers. Combining DORA with other engineering analytics is a great way to gain a complete picture of the development process. It may include identifying bottlenecks and improvement areas. It may include including identifying bottlenecks and improvement areas.

Use Other Indexes along with DORA Metrics

However, poor interpretation of DORA data can occur due to the lack of uniformity in defining failure, which is a challenge for metrics like CFR and MTTR. Using custom information to interpret the results is often ineffective. Additionally, DORA metrics only focus on velocity and stability. It does not consider other factors such as the quality of work, productivity of developers, and the impact on the end-user. So, it is important to use other indexes for a proactive response, qualitative analysis of workflows, and SDLC predictability. It will help to gain a 360-degree profiling of the team’s workflow.

Use it as a Tool for Continuous Improvement and Increase Value Delivery

To achieve business goals, it is essential to correlate DORA data with other critical indicators like review time, code churn, maker time, PR size, and more. Using DORA in combination with more context, customization, and traceability can offer valuable insights and a true picture of the team’s performance and identify the steps needed to resolve bottlenecks and hidden fault lines at all levels. Ultimately, DORA should be used as a tool for continuous improvement, product management, and enhancing value delivery.

DORA metrics can also provide insights into coding skills by revealing patterns related to code quality, review effectiveness, and debugging cycles. This can help to identify the blind spots where additional training is required.

How Typo Leverages DORA Metrics?

Typo is a powerful engineering analytics tool for tracking and analyzing DORA metrics. It provides an efficient solution for software development teams seeking precision in their DevOps performance measurement and delivers high-quality software to end users.

  • With pre-built integrations in the dev tool stack, the DORA metrics dashboard provides all the relevant data flowing in within minutes.
  • It helps in deep diving and correlating different metrics to identify real-time bottlenecks, sprint delays, blocked PRs, deployment efficiency, and much more from a single dashboard.
  • The dashboard sets custom improvement goals for each team and tracks their success in real-time to boost organizational performance.
  • It gives real-time visibility into a team’s KPI and lets them make informed decisions.

Conclusion

While DORA serves its purpose well, it is only the beginning of improving engineering excellence. To effectively measure DORA metrics, it is essential to focus on key DORA metrics and the business value they provide. Looking at numbers alone is not enough. Engineering managers should also focus on the practices and people behind the numbers and the barriers they face to achieve their best and ensure customer satisfaction. It is a known fact that engineering excellence is related to a team’s productivity and well-being. So, an effective way is to consider all factors that impact a team’s performance and take appropriate steps to address them.

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