Software Delivery

Top Platform Engineering KPIs You Need to Monitor

Platform Engineering is becoming increasingly crucial. According to the 2024 State of DevOps Report: The Evolution of Platform Engineering, 43% of organizations have had platform teams for 3-5 years. The field offers numerous benefits, such as faster time-to-market, enhanced developer happiness, and the elimination of team silos.

However, there is one critical piece of advice that Platform Engineers often overlook: treat your platform as an internal product and consider your wider teams as your customers.

So, how can they do this effectively? It's important to measure what’s working and what isn’t using consistent indicators of success.

In this blog, we’ve curated the top platform engineering KPIs that software teams must monitor:

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 Tracking Platform Engineering KPIs

Helps in Performance Monitoring and Optimization

Platform Engineering KPIs offer insights into how well the platform performs under various conditions. They also help to identify loopholes and areas that need optimization to ensure the platform runs efficiently.

Ensures Scalability and Capacity Planning

These metrics guide decisions on how to scale resources. It also ensures the capacity planning i.e. the platform can handle growth and increased load without performance degradation. 

Quality Assurance

Tracking KPIs ensure that the platform remains robust and maintainable. This further helps to reduce technical debt and improve the platform’s overall quality. 

Increases Productivity and Collaboration

They provide in-depth insights into how effectively the engineering team operates and help to identify areas for improvement in team dynamics and processes.

Fosters a Culture of Continuous Improvement

Regularly tracking and analyzing KPIs fosters a culture of continuous improvement. Hence, encouraging proactive problem-solving and innovation among platform engineers. 

Top Platform Engineering KPIs to Track 

Deployment Frequency 

Deployment Frequency measures how often code is deployed into production per week. It takes into account everything from bug fixes and capability improvements to new features. It is a key metric for understanding the agility and efficiency of development and operational processes and highlights the team’s ability to deliver updates and new features.

The higher frequency with minimal issues reflects mature CI/CD processes and how platform engineering teams can quickly adapt to changes. Regularly tracking and adapting Deployment Frequency helps in continuous improvement as it reduces the risk of large, disruptive changes and delivers value to end-users effectively. 

Lead Time for Changes

Lead Time is the duration between a code change being committed and its successful deployment to end-users. It is correlated with both the speed and quality of the platform engineering team. Higher lead time gives a clear sign of roadblocks in processes and the platform needs attention. 

Low lead time indicates that the teams quickly adapt to feedback and deliver products timely. It also gives teams the ability to make rapid changes, allowing them to adapt to evolving user needs and market conditions. Tracking it regularly helps in streamlining workflows and reducing bottlenecks. 

Change Failure Rate

Change Failure Rate refers to the proportion or percentage of deployments that result in failure or errors. It indicates the rate at which changes negatively impact the stability or functionality of the system. CFR also provides a clear view of the platform’s quality and stability eg: how much effort goes into addressing problems and releasing code.

Lower CFR indicates that deployments are reliable, changes are thoroughly tested, and less likely to cause issues in production. Moreover, it also reflects a well-functioning development and deployment processes, boosting team confidence and morale. 

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.  Low MTTR indicates that the platform is resilient, quickly recovers from issues, and efficiency of incident response. 

Faster recovery time minimizes the impact on users, increasing their satisfaction and trust in service. Moreover, it contributes to higher system uptime and availability and enhances your platform’s reputation, giving you a competitive edge. 

Resource Utilization 

This KPI tracks the usage of system resources. It is a critical metric that optimizes resource allocation and cost efficiency. Resource Utilization balances several objectives with a fixed amount of resources. 

It allows platform engineers to distribute limited resources evenly and efficiently and understand where exactly to spend. Resource Utilization also aids in capacity planning and helps in avoiding potential bottlenecks. 

Error Rates

Error Rates measure the number of errors encountered in the platform. It identifies the stability, reliability, and user experience of the platform. High Error Rates indicate underlying problems that need immediate attention which can otherwise, degrade user experience, leading to frustration and potential loss of users.

Monitoring Error Rates helps in the early detection of issues, enabling proactive response, and preventing minor issues from escalating into major outages. It also provides valuable insights into system performance and creates a feedback loop that informs continuous improvement efforts. 

Team Velocity 

Team Velocity is a critical metric that measures the amount of work completed in a given iteration (e.g., sprint). It highlights the developer productivity and efficiency as well as in planning and prioritizing future tasks. 

It helps to forecast the completion dates of larger projects or features, aiding in long-term planning and setting stakeholder expectations. Team Velocity also helps to understand the platform teams’ capacity to evenly distribute tasks and prevent overloading team members. 

How to Develop a Platform Engineering KPI Plan? 

Define Objectives 

Firstly, ensure that the KPIs support the organization’s broader objectives. A few of them include improving system reliability, enhancing user experience, or increasing development efficiency. Always focus on metrics that reflect the unique aspects of platform engineering. 

Identify Key Performance Indicators 

Select KPIs that provide a comprehensive view of platform engineering performance. We’ve shared some critical KPIs above. Choose those KPIs that fit your objectives and other considered factors. 

Establish Baseline and Targets

Assess current performance levels of software engineers to establish baselines. Set targets and ensure they are realistic and achievable for each KPI. They must be based on historical data, industry benchmarks, and business objectives.

Analyze and Interpret Data

Regularly analyze trends in the data to identify patterns, anomalies, and areas for improvement. Set up alerts for critical KPIs that require immediate attention. Don’t forget to conduct root cause analysis for any deviations from expected performance to understand underlying issues.

Review and Refine KPIs

Lastly, review the relevance and effectiveness of the KPIs periodically to ensure they align with business objectives and provide value. Adjust targets based on changes in business goals, market conditions, or team capacity.

Typo - An Effective Platform Engineering Tool 

Typo is an effective platform engineering tool 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.


Monitoring the right KPIs is essential for successful platform teams. By treating your platform as an internal product and your teams as customers, you can focus on delivering value and driving continuous improvement. The KPIs discussed above, provide a comprehensive view of your platform's performance and areas for enhancement. 

There are other KPIs available as well that we have not mentioned. Do your research and consider those that best suit your team and objectives.

All the best! 

Comparative Analysis of DevOps and Platform Engineering

There are two essential concepts in contemporary software engineering: DevOps and Platform Engineering.

In this article, We dive into how DevOps has revolutionized the industry, explore the emerging role of Platform Engineering, and compare their distinct methodologies and impacts.

What is DevOps?

DevOps is a cultural and technical movement aimed at unifying software development (Dev) and IT operations (Ops) to improve collaboration, streamline processes, and enhance the speed and quality of software delivery. The primary goal of DevOps is to create a more cohesive, continuous workflow from development through to production.

Key Principles of DevOps

  • Automation: Automating repetitive tasks to increase efficiency and reduce errors.
  • Continuous Integration and Continuous Delivery (CI/CD): Integrating code changes frequently and automating the deployment process to ensure rapid, reliable releases.
  • Collaboration and Communication: Fostering a culture of shared responsibility between development and operations teams.
  • Monitoring and Logging: Continuously monitoring applications and infrastructure to identify issues early and improve performance.
  • Infrastructure as Code (IaC): Managing and provisioning computing infrastructure through machine-readable definition files.

What is Platform Engineering?

Platform engineering is the practice of designing and building toolchains and workflows that enable self-service capabilities for software engineering organizations in the cloud-native era. It focuses on creating internal developer platforms (IDPs) that provide standardized environments and services for development teams.

Key Principles of Platform Engineering

  • Self-Service Interfaces: Providing developers with easy access to environments, tools, and infrastructure.
  • Standardization and Consistency: Ensuring that environments and workflows are consistent across different projects and teams.
  • Scalability and Flexibility: Designing platforms that can scale with organizational needs and accommodate different technologies and workflows.
  • Security and Compliance: Embedding security and compliance checks within the platform to ensure that applications meet organizational and regulatory standards.
  • Developer Experience: Improving the overall developer experience by reducing friction and enabling faster delivery cycles.

Comparative Analysis of DevOps and Platform Engineering


Technical Foundations

Architectural Differences

Toolchains and Technologies

Processes and Workflows

Operational Impact


DevOps and Platform Engineering offer different yet complementary approaches to enhancing software development and delivery. DevOps focuses on cultural integration and automation, while Platform Engineering emphasizes providing a robust, scalable infrastructure platform. By understanding these technical distinctions, organizations can make informed decisions to optimize their software development processes and achieve their operational goals.

How to Become a Successful Platform Engineer

Platform engineering is a relatively new and evolving field in the tech industry. While it offers many opportunities, certain aspects are often overlooked.

In this blog, we discuss effective strategies for becoming a successful platform engineer and identify common pitfalls to avoid.

What is a Platform Engineer? 

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. 

Strategies for Being a Great Platform Engineer

Keeping the Entire Engineering Organization Up-to-Date with Platform Team Insights

One important tip to becoming a great platform engineer is informing the entire engineering organization about platform team initiatives. This fosters transparency, alignment, and cross-team collaboration, ensuring everyone is on the same page. When everyone is aware of what’s happening in the platform team, it allows them to plan tasks effectively, offer feedback, raise concerns early, and minimize duplication of efforts. As a result, providing everyone a shared understanding of the platform, goals, and challenges. 

Ensure Your Team Possesses Diverse Skills

When everyone on the platform engineering team has varied skill sets, it brings a variety of perspectives and expertise to the table. This further helps in solving problems creatively and approaching challenges from multiple angles. 

It also lets the team handle a wide range of tasks such as architecture design and maintenance effectively. Furthermore, team members can also learn from each other (and so do you!) which helps in better collaboration and understanding and addressing user needs comprehensively.

Automate as much as Possible 

Pull Requests and code reviews, when done manually, take a lot of the team’s time and effort. Hence, this gives you an important reason why to use automation tools. Moreover, it allows you to focus on more strategic and high-value tasks and lets you handle an increased workload. This further helps in accelerating development cycles and time to market for new features and updates which optimizes resource utilization and reduces operational costs over time. 

Maintain a DevOps Culture 

Platform engineering isn’t all about building the underlying tools, it also signifies maintaining a DevOps culture. You must partner with development, security, and operations teams to improve efficiency and performance. This allows for having the right conversation for discovering bottlenecks, and flexibility in tool choices, and reinforces positive collaboration among teams. 

Moreover, it encourages a feedback-driven culture, where teams can continuously learn and improve. As a result, aligning the team’s efforts closely with customer requirements and business objectives. 

Stay Abreast of Industry Trends

To be a successful platform engineer, it's important to stay current with the latest trends and technologies. Attending tech workshops, webinars, and conferences is an excellent way to keep up with industry developments. besides these offline methods, you can read blogs, follow tech influencers, listen to podcasts, and join online discussions to improve your knowledge and stay ahead of industry trends.

Moreover, collaborating with a team that possesses diverse skill sets can help you identify areas that require upskilling and introduce you to new tools, frameworks, and best practices. This combined approach enables you to better anticipate and meet customer needs and expectations.

Take Everything into Consideration and Measure Success Holistically 

Beyond DevOps metrics, consider factors like security improvements, cost optimization, and consistency across the organization. This holistic approach prevents overemphasis on a single area and helps identify potential risks and issues that might be overlooked when focusing solely on individual metrics. Additionally, it highlights areas for improvement and drives ongoing optimized efficiencies across all dimensions of the platform.

Common Pitfalls that Platform Engineers Ignore 

Unable to Understand the Right Customers 

First things first, understand who your customers are. When platform teams prioritize features or improvements that don't meet software developers' needs, it negatively impacts their user experience. This can lead to poor user interfaces, inadequate documentation, and missing functionalities, directly affecting customers' productivity.

Therefore, it's essential to identify the target audience, understand their key requirements, and align with their requests. Ignoring this in the long run can result in low usage rates and a gradual erosion of customer trust.

Lack of Adequate Tools for Platform Teams

One of the common mistakes that platform engineers make is not giving engineering teams enough tooling or ownership. This makes it difficult for them to diagnose and fix issues in their code. It increases the likelihood of errors and downtime as teams may struggle to thoroughly test and monitor code. Not only this, they may also struggle to spend more time on manual processes and troubleshooting which slows down the development time cycle. 

Hence, it is always advisable to provide your team with enough tooling. Discuss with them what tooling they need, whether the existing ones are working fine, and what requirements they have. 

Too Much Planning, Not Enough Feature Releases

When a lot of time is spent on planning, it results in analysis paralysis i.e. thinking too much of potential features and improvements rather than implementation and testing. This further results in delays in deliveries, hence, slowing down the development process and feedback loops. 

Early and frequent shipping creates the right feedback loops that can enhance the user experience and improve the platform continuously. These feature releases must be prioritized based on how often certain deployment proceedings are performed. Make sure to involve the software developers as well to discover more effective solutions. 

Neglecting the Documentation Process

The documentation process is often underestimated. Platform engineers believe that the process is self-explanatory but this isn’t true. Everything around code, feature releases, and related to the platform must be comprehensively documented. This is critical for onboarding, troubleshooting, and knowledge transfer. 

Well-written documents also help in establishing and maintaining consistent practices and standards across the team. It also allows an understanding of the system’s architecture, dependencies, and known issues. 

Relying Solely on Third Party Tools for Security

Platform engineers must take full ownership of security issues. Lack of accountability can result in increased security risks and vulnerabilities specific to the platform. The limited understanding of unique risks and vulnerabilities can affect the system. 

But that doesn’t mean that platform engineers must stop using third-party tools. They must leverage them however, they need to be complemented by internal processes or knowledge and need to be integrated into the design, development, and deployment phases of platform engineering.  

Typo - An Effective Platform Engineering Tool 

Typo is an effective platform engineering tool 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.


Implementing these strategies will improve your success as a platform engineer. By prioritizing transparency, diverse skill sets, automation, and a DevOps culture, you can build a robust platform that meets evolving needs efficiently. Staying updated with industry trends and taking a holistic approach to success metrics ensures continuous improvement.

Ensure to avoid the common pitfalls as well. By addressing these challenges, you create a responsive, secure, and innovative platform environment.

Hope this helps. All the best! :)

Impact of DORA Metrics on SPACE Efficiency in Software Development


Efficiency in software development is crucial for delivering high-quality products quickly and reliably. This research investigates the impact of DORA (DevOps Research and Assessment) Metrics — Deployment Frequency, Lead Time for Changes, Mean Time to Recover (MTTR), and Change Failure Rate — on efficiency within the SPACE framework (Satisfaction, Performance, Activity, Collaboration, Efficiency). Through detailed mathematical calculations, correlation with business metrics, and a case study of one of our customers, this study provides empirical evidence of their influence on operational efficiency, customer satisfaction, and financial performance in software development organizations.


Efficiency is a fundamental aspect of successful software development, influencing productivity, cost-effectiveness, and customer satisfaction. The DORA Metrics serve as standardized benchmarks to assess and enhance software delivery performance across various dimensions. This paper aims to explore the quantitative impact of these metrics on SPACE efficiency and their correlation with key business metrics, providing insights into how organizations can optimize their software development processes for competitive advantage.

Literature Review

Previous research has highlighted the significance of DORA Metrics in improving software delivery performance and organizational agility (Forsgren et al., 2020). However, detailed empirical studies demonstrating their specific impact on SPACE efficiency and business metrics remain limited, warranting comprehensive analysis and calculation-based research.


Case Study Design: one of our customers in the US — A B2B SaaS Company with 120+ Engineers

Selection Criteria: A leading SaaS company based in the US, was chosen for this case study due to its scale and complexity in software development operations. With over 120 engineers distributed across various teams, the customer faced challenges related to deployment efficiency, reliability, and customer satisfaction.

Data Collection: Utilized the customer’s internal metrics and tools, including deployment logs, incident reports, customer feedback surveys, and performance dashboards. The study focused on a period of 12 months to capture seasonal variations and long-term trends in software delivery performance.

Contextual Insights: Gathered qualitative insights through interviews with the customer’s development and operations teams. These interviews provided valuable context on existing challenges, process bottlenecks, and strategic goals for improving software delivery efficiency.

Selection and Calculation of DORA Metrics

Deployment Frequency: Calculated as the number of deployments per unit time (e.g., per day).

Example: They increased their deployment frequency from 3 deployments per week to 15 deployments per week during the study period.


Insight: Higher deployment frequency facilitated faster feature delivery and responsiveness to market demands.

Lead Time for Changes: Measured from code commit to deployment completion.

Example: Lead time reduced from 7 days to 1 day due to process optimizations and automation efforts.


Insight: Shorter lead times enabled TYPO’s customer to swiftly adapt to customer feedback and market changes.

MTTR (Mean Time to Recover): Calculated as the average time taken to restore service after an incident.

Example: MTTR decreased from 4 hours to 30 minutes through improved incident response protocols and automated recovery mechanisms.


Insight: Reduced MTTR enhanced system reliability and minimized service disruptions.

Change Failure Rate: Determined by dividing the number of failed deployments by the total number of deployments.

Example: Change failure rate decreased from 8% to 1% due to enhanced testing protocols and deployment automation.

Insight: Lower change failure rate improved product stability and customer satisfaction.

Correlation with Business Metrics

Revenue Growth: TYPO’s customer achieved a 25% increase in revenue attributed to faster time-to-market and improved customer satisfaction.

Customer Satisfaction: Improved Net Promoter Score (NPS) from 8 to 9, indicating higher customer loyalty and retention rates.

Employee Productivity: Increased by 30% as teams spent less time on firefighting and more on innovation and feature development.


The findings from our customer case study illustrate a clear correlation between improved DORA Metrics, enhanced SPACE efficiency, and positive business outcomes. By optimizing Deployment Frequency, Lead Time for Changes, MTTR, and Change Failure Rate, organizations can achieve significant improvements in operational efficiency, customer satisfaction, and financial performance. These results underscore the importance of data-driven decision-making and continuous improvement practices in software development.

How Typo Leverages DORA Metrics?

Typo 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. 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.


In conclusion, leveraging DORA Metrics within software development processes enables organisations to streamline operations, accelerate innovation, and maintain a competitive edge in the market. By aligning these metrics with business objectives and systematically improving their deployment practices, companies can achieve sustainable growth and strategic advantages. Future research should continue to explore emerging trends in DevOps and their implications for optimizing software delivery performance.

Next Steps

Moving forward, Typo and similar organizations consider the following next steps based on the insights gained from this study:

  • Continuous Optimization: Implement continuous optimization practices to further enhance DORA Metrics and sustain efficiency gains.
  • Expansion of Metrics: Explore additional DORA Metrics and benchmarks to capture broader aspects of software delivery performance.
  • Industry Collaboration: Engage in industry collaborations and benchmarking exercises to validate and benchmark performance against peers.
  • Technology Integration: Invest in advanced technologies such as AI and machine learning to automate and optimize software delivery processes further.


  • Forsgren, N., Humble, J., & Kim, G. (2020). Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations. IT Revolution Press.

State of DevOps Report 2023 Highlights

Although we are somewhat late in presenting this summary, the insights from the 2023 State of DevOps Report remain highly relevant and valuable for the industry. The DevOps Research and Assessment (DORA) program has significantly influenced software development practices over the past decade. Each year, the State of DevOps Report provides a detailed analysis of the practices and capabilities that drive success in software delivery, offering benchmarks that teams can use to evaluate their own performance. This blog summarizes the key findings from the 2023 report, incorporates additional data and insights from industry developments, and introduces the role of the Software Engineering Institute (SEI) platform as highlighted by Gartner in 2024.

Overview of the 2023 Report

The 2023 State of DevOps Report draws from responses provided by over 36,000 professionals across various industries and organizational sizes. This year’s research emphasizes three primary outcomes:

  1. Organizational Performance: Generating value for customers and the community, extending beyond just revenue metrics.
  2. Team Performance: Evaluating teams’ ability to innovate and collaborate effectively.
  3. Employee Well-being: Assessing the impact of organizational strategies on reducing burnout and enhancing job satisfaction and productivity.

Additionally, the report examines two key performance measures:

  • Software Delivery Performance: The efficiency and safety of teams in implementing changes in technology systems.
  • Operational Performance: The reliability and quality of the user experience provided.

Key Takeaways from the Report

Culture Is Critical

The 2023 report highlights the crucial role of culture in developing technical capabilities and driving performance. Teams with a generative culture — characterized by high levels of trust, autonomy, open information flow, and a focus on learning from failures rather than assigning blame — achieve, on average, 30% higher organizational performance. This type of culture is essential for fostering innovation, collaboration, and continuous improvement.

Building a successful organizational culture requires a combination of everyday practices and strategic leadership. Practitioners shape culture through their daily actions, promoting collaboration and trust. Transformational leadership is also vital, emphasizing the importance of a supportive environment that encourages experimentation and autonomy.

User-Centric Approach

A significant finding in this year’s report is that a user-centric approach to software development is a strong predictor of organizational performance. Teams with a strong focus on user needs show 40% higher organizational performance and a 20% increase in job satisfaction. Leaders can foster an environment that prioritizes user value by creating incentive structures that reward teams for delivering meaningful user value rather than merely producing features.

Generative AI: Early Stages

An intriguing insight from the report is that the use of Generative AI, such as coding assistants, has not yet shown a significant impact on performance. This is likely because larger enterprises are slower to adopt emerging technologies. However, as adoption increases and more data becomes available, this trend is expected to evolve.

Investing in Technical Capabilities

Investing in technical capabilities like continuous integration and delivery, trunk-based development, and loosely coupled architectures leads to substantial improvements in performance. For example, reducing code review times can improve software delivery performance by up to 50%. High-quality documentation further enhances these technical practices, with trunk-based development showing a 12.8x greater impact on organizational performance when supported by quality documentation.

Cloud Computing Enhances Flexibility

Leveraging cloud platforms significantly enhances flexibility and, consequently, performance. Using a public cloud platform increases infrastructure flexibility by 22% compared to other environments. While multi-cloud strategies also improve flexibility, they can introduce complexity in managing governance, compliance, and risk. To maximize the benefits of cloud computing, organizations should modernize and refactor workloads to exploit the cloud’s flexibility rather than simply migrating existing infrastructure.

Equitable Work Distribution

The report indicates that individuals from underrepresented groups, including women and those who self-describe their gender, experience higher levels of burnout and are more likely to engage in repetitive work. Implementing formal processes to distribute work evenly can help reduce burnout. However, further efforts are needed to extend these benefits to all underrepresented groups.

Flexible Working Arrangements

The Covid-19 pandemic has reshaped working arrangements, with many employees working remotely. About 33% of respondents in this year’s survey work exclusively from home, while 63% work from home more often than an office. Although there is no conclusive evidence that remote work impacts team or organizational performance, flexibility in work arrangements correlates with increased value delivered to users and improved employee well-being. This flexibility also applies to new hires, with no observable increase in performance linked to office-based onboarding.

Actual Practices and Trends in DevOps

The 2023 report highlights several key practices that are driving success in DevOps:

️Continuous Integration/Continuous Delivery (CI/CD)

Implementing CI/CD pipelines is essential for automating the integration and delivery process. This practice allows teams to detect issues early, reduce integration problems, and deliver updates more frequently and reliably.

  • Google: Google has implemented CI/CD pipelines extensively across its development teams. This practice has enabled Google to push thousands of updates daily with minimal disruption. Automated testing and deployment ensure that new code is integrated seamlessly, significantly reducing the risk of integration issues.
  • Netflix: Known for its high-frequency deployments, Netflix utilizes a CI/CD pipeline that includes automated testing, canary releases, and real-time monitoring. This approach allows Netflix to deliver new features and updates quickly while maintaining high reliability and performance.

️ Trunk-Based Development

This approach involves developers integrating their changes into a shared trunk frequently, reducing the complexity of merging code and improving collaboration. Trunk-based development is linked to faster delivery cycles and higher quality outputs.

  • Facebook: Facebook employs trunk-based development to streamline code integration. Developers frequently merge their changes into the main branch, reducing merge conflicts and integration pain. This practice supports Facebook’s fast-paced release cycles, enabling frequent updates without compromising stability.
  • Etsy: Etsy has adopted trunk-based development to foster collaboration and accelerate delivery. By continuously integrating code into the main branch, Etsy’s development teams can quickly address issues and deliver new features, enhancing their agility and responsiveness to market demands.

️Loosely Coupled Architectures

Designing systems as loosely coupled services or microservices helps teams develop, deploy, and scale components independently. This architecture enhances system resilience and flexibility, enabling faster and more reliable updates.

  • Amazon: Amazon’s architecture is built around microservices, allowing teams to develop, deploy, and scale services independently. This decoupled architecture enhances system resilience and flexibility, enabling Amazon to innovate rapidly and handle high traffic volumes efficiently.
  • Spotify: Spotify uses microservices to ensure that different parts of its application can be updated independently. This architecture allows Spotify to scale its services globally, providing a reliable and high-quality user experience even during peak usage times.

️Automated Testing

Automated testing is critical for maintaining high-quality code and ensuring that new changes do not introduce defects. This practice supports continuous delivery by providing immediate feedback on code quality.

  • Microsoft: Microsoft has integrated automated testing into its development pipeline for products like Azure. Automated unit, integration, and end-to-end tests ensure that new code meets quality standards before it is deployed, reducing the risk of defects and improving overall software quality.
  • Airbnb: Airbnb relies heavily on automated testing to maintain the quality of its platform. By incorporating automated tests into their CI/CD pipeline, Airbnb can rapidly identify and address issues, ensuring that new features are delivered without introducing bugs.

️Monitoring and Observability

Implementing robust monitoring and observability practices allows teams to gain insights into system performance and user behavior. These practices help in quickly identifying and resolving issues, improving system reliability and user satisfaction.

  • LinkedIn: LinkedIn has developed a comprehensive observability platform that provides real-time insights into system performance and user behavior. This platform helps LinkedIn quickly identify and resolve issues, improving system reliability and enhancing the user experience.
  • Uber: Uber uses advanced monitoring and observability tools to track the performance of its services. These tools provide detailed metrics and alerts, enabling Uber to proactively manage system health and ensure a seamless experience for users.

️Infrastructure as Code (IaC)

Using IaC enables teams to manage and provision infrastructure through code, making the process more efficient, repeatable, and less prone to human error. IaC practices contribute to faster, more consistent deployment of infrastructure resources.

  • Capital One: Capital One has adopted Infrastructure as Code to automate the provisioning and management of its cloud infrastructure. Using tools like AWS CloudFormation and Terraform, Capital One ensures consistency, reduces manual errors, and accelerates infrastructure deployment.
  • Shopify: Shopify employs IaC to manage its infrastructure across multiple cloud providers. This approach allows Shopify to maintain a consistent and repeatable deployment process, supporting rapid scaling and reducing the time required to provision new environments.

The Role of Metrics and Insights

Metrics are vital for guiding teams and driving continuous improvement. However, they should be used to inform and guide rather than set rigid targets, in accordance with Goodhart’s law. Here’s why metrics are crucial:

  • Promoting Accountability and Transparency: Metrics foster a culture of ownership and responsibility, creating transparency and shared goals within teams.
  • Enabling Data-Driven Decisions: Metrics provide objective data for evaluating processes, identifying inefficiencies, and implementing improvements.
  • Facilitating Collaboration and Communication: Shared metrics create a common understanding, making it easier for teams to collaborate effectively.
  • Supporting Continuous Improvement: Regularly measuring and analyzing performance helps teams identify trends, address inefficiencies, and continuously improve.

SEI Platform as Highlighted by Gartner

The Software Engineering Intelligence(SEI) platforms like Typo , as highlighted in Gartner’s research, plays a pivotal role in advancing DevOps practices. The SEI platform provides tools and frameworks that help organizations assess their software engineering capabilities and identify areas for improvement. This platform emphasizes the importance of integrating DevOps principles into the entire software development lifecycle, from initial planning to deployment and maintenance.

Gartner’s analysis indicates that organizations leveraging the SEI platform see significant improvements in their DevOps maturity, leading to enhanced performance, reduced time to market, and increased customer satisfaction. The platform’s comprehensive approach ensures that DevOps practices are not just implemented but are continuously optimized to meet evolving business needs.


The State of DevOps Report 2023 by DORA offers critical insights into the current state of DevOps, emphasizing the importance of culture, user focus, technical capabilities, cloud flexibility, and equitable work distribution.

Further Reading

For those interested in delving deeper into the State of DevOps Report 2023 and related topics, here are some recommended resources:

These resources provide extensive insights into DevOps principles and practices, offering practical guidance for organizations aiming to enhance their DevOps capabilities and achieve greater success in their software delivery processes.

Understanding the hurdles in sprint reviews

Sprint reviews aim to foster open communication, active engagement, alignment with goals, and clear expectations. Despite these noble goals, many teams face significant hurdles in achieving them. These challenges often stem from the complexities involved in managing these elements effectively.

Common issues in sprint reviews

  • Open Communication: One of the core principles of Agile is transparent and timely information sharing. However, developers often hesitate to provide early feedback due to the fear of premature criticism. This delay in communication can hinder problem-solving and allow minor issues to escalate. Moreover, sprint reviews sometimes become dominated by the Product Owner, overshadowing the collaborative efforts of the entire team.
  • Engagement: For sprint reviews to be effective, active participation from all team members and stakeholders is crucial. Unfortunately, these meetings often become monotonous, with one person presenting while others remain passive. This one-sided narrative stifles the collective intelligence of the group and diminishes the value of the meeting.
  • Goal Alignment: Clear, collaboratively set goals are essential in Agile. These goals provide direction and purpose for the team’s efforts. However, without frequent reinforcement, teams can lose focus. Developers may pursue interesting but unrelated tasks that, while beneficial on their own, can detract from the primary objectives of the sprint. This issue is compounded by unclear definitions of what constitutes “done,” leading to incomplete tasks being presented as finished.
  • Managing Expectations: Misaligned expectations can derail sprint reviews. For instance, if stakeholders expect these meetings to be approval sessions or if developers dive too deeply into unnecessary details, the main points can become obscured, reducing the effectiveness of the meeting.

Strategies for effective sprint reviews

To overcome these challenges, teams should adopt a set of best practices designed to enhance the efficiency and productivity of sprint reviews. The following principles provide a framework for achieving this:

Cultivate open communication

Continuous dialogue is the cornerstone of Agile methodology. For sprint reviews to be effective, a culture of open communication must be established and ingrained in daily interactions. Leaders play a crucial role in fostering an environment where team members feel safe to share concerns and challenges without fear of repercussions. This approach minimizes friction and ensures issues are addressed promptly before they escalate.

Case Study: Atlassian, a leading software company, introduced regular, open discussions about project hurdles. This practice fostered a culture of transparency, allowing the team to address potential issues early and leading to more efficient sprint reviews. As a result, they saw a 30% reduction in unresolved issues by the end of each sprint.

Promote active and inclusive engagement

Sprint reviews should be interactive sessions with two-way communication. Instead of having a single person present, these meetings should involve contributions from all team members. Passing the keyboard around and encouraging real-time discussions can make the review more dynamic and collaborative.

Case Study: HubSpot, a marketing and sales software company, transformed their sprint reviews by making them more interactive. During brainstorming sessions for new campaigns, involving all team members led to more innovative solutions and a greater sense of ownership and engagement across the team. HubSpot reported a 25% increase in team satisfaction scores and a 20% increase in creative solutions presented during sprint reviews.

Set, reinforce, and stick to goals

While setting clear goals is essential, the real challenge lies in revisiting and realigning them throughout the sprint. Regular check-ins with both internal teams and stakeholders help maintain focus and ensure consistency.

Case Study: Epic Systems, a healthcare software company, improved their sprint reviews by regularly revisiting their primary goal of enhancing user experience. By ensuring that all new features and changes aligned with this objective, they were able to maintain focus and deliver a more cohesive product. This led to a 15% increase in user satisfaction ratings and a 10% reduction in feature revisions post-launch.

Ensure clarity in expectations

Effective sprint reviews require clear and mutual understanding. Teams must ensure they are not just explaining but also being understood. Setting the context at the beginning of each meeting, followed by a quick recap of previous interactions, can bridge any gaps.

Case Study: FedEx, a logistics giant, faced challenges with misaligned expectations during sprint reviews. Stakeholders often expected these meetings to be approval sessions, which led to confusion and inefficiency. To address this, FedEx started each sprint review with a clear definition of expectations and a quick recap of previous interactions. This approach ensured that all team members and stakeholders were aligned on objectives and progress, making the discussions more productive. Consequently, FedEx experienced a 20% reduction in project delays and a 15% improvement in stakeholder satisfaction.

Additional strategies for enhancing sprint reviews

Beyond the foundational principles of open communication, engagement, goal alignment, and clear expectations, there are additional strategies that can further enhance the effectiveness of sprint reviews:

Leverage data and metrics

Using data and metrics to track progress can provide objective insights into the team’s performance and highlight areas for improvement. Tools like burn-down charts, velocity charts, and cumulative flow diagrams can be invaluable in providing a clear picture of the team’s progress and identifying potential bottlenecks.

Example: Capital One, a financial services company, used velocity charts to track their sprint progress. By analyzing the data, they were able to identify patterns and trends, which helped them optimize their workflow and improve overall efficiency. They reported a 22% increase in on-time project completion and a 15% decrease in sprint overruns.

Incorporate feedback loops

Continuous improvement is a key tenet of Agile. Incorporating feedback loops within sprint reviews can help teams identify areas for improvement and implement changes more effectively. This can be achieved through regular retrospectives, where the team reflects on what went well, what didn’t, and how they can improve.

Example: Amazon, an e-commerce giant, introduced regular retrospectives at the end of each sprint review. By discussing successes and challenges, they were able to implement changes that significantly improved their workflow and product quality. This practice led to a 30% increase in overall team productivity and a 25% improvement in customer satisfaction ratings.

Facilitate stakeholder involvement

Involving stakeholders in sprint reviews can provide valuable insights and ensure that the team is aligned with business objectives. Stakeholders can offer feedback on the product’s direction, validate the team’s progress, and provide clarity on priorities.

Example: Google started involving stakeholders in their sprint reviews. This practice helped ensure that the team’s work was aligned with business goals and that any potential issues were addressed early. Google reported a 20% improvement in project alignment with business objectives and a 15% decrease in project scope changes.

Real-life case studies

Case study 1: Enhancing communication at Atlassian

Atlassian, a leading software company, faced significant challenges with communication during sprint reviews. Developers were hesitant to share early feedback, which led to delayed problem-solving and escalated issues. The team decided to implement daily check-in meetings where all members could discuss ongoing challenges openly. This practice fostered a culture of transparency and ensured that potential issues were addressed promptly. As a result, the team’s sprint reviews became more efficient, and their overall productivity improved. Atlassian saw a 30% reduction in unresolved issues by the end of each sprint and a 25% increase in overall team morale.

Case Study 2: Boosting engagement at HubSpot

HubSpot, a marketing and sales software company, struggled with engagement during their sprint reviews. Meetings were often dominated by a single presenter, with little input from other team members. To address this, HubSpot introduced interactive brainstorming sessions during sprint reviews, where all team members were encouraged to contribute ideas. This change led to more innovative solutions and a greater sense of ownership and engagement among the team. HubSpot reported a 25% increase in team satisfaction scores, a 20% increase in creative solutions presented during sprint reviews, and a 15% decrease in time to market for new features.

Case Study 3: Aligning goals at Epic Systems

Epic Systems, a healthcare software company, had difficulty maintaining focus on their primary goal of enhancing user experience. Developers frequently pursued interesting but unrelated tasks. The company decided to implement regular check-ins to revisit and realign their goals. This practice ensured that all new features and changes were in line with the overarching objective, leading to a more cohesive product and improved user satisfaction. As a result, Epic Systems experienced a 15% increase in user satisfaction ratings, a 10% reduction in feature revisions post-launch, and a 20% improvement in overall product quality.

Case Study 4: Clarifying expectations at FedEx

FedEx, a logistics giant, faced challenges with misaligned expectations during sprint reviews. Stakeholders often expected these meetings to be approval sessions, which led to confusion and inefficiency. To address this, FedEx started each sprint review with a clear definition of expectations and a quick recap of previous interactions. This approach ensured that all team members and stakeholders were aligned on objectives and progress, making the discussions more productive. Consequently, FedEx experienced a 20% reduction in project delays, a 15% improvement in stakeholder satisfaction, and a 10% increase in overall team efficiency.

Incorporating data and statistics

Data and metrics can provide valuable insights into the effectiveness of sprint reviews. For example, according to a report by VersionOne, 64% of Agile teams use burn-down charts to track their progress. These charts can highlight trends and potential bottlenecks, helping teams optimize their workflow.

Additionally, a study by the Project Management Institute (PMI) found that organizations that use Agile practices are 28% more successful in their projects compared to those that do not. This statistic underscores the importance of implementing effective Agile practices, including efficient sprint reviews.


Sprint reviews are a critical component of the Agile framework, designed to ensure that teams stay aligned on goals and progress. By addressing common challenges such as communication barriers, lack of engagement, misaligned goals, and unclear expectations, teams can significantly improve the effectiveness of their sprint reviews.

Implementing strategies such as fostering open communication, promoting active engagement, setting and reinforcing goals, ensuring clarity in expectations, leveraging data and metrics, incorporating feedback loops, and facilitating stakeholder involvement can transform sprint reviews into highly productive sessions.

By learning from real-life case studies and incorporating data-driven insights, teams can continuously improve their sprint review process, leading to better project outcomes and greater overall success.

Moving beyond JIRA sprint reports in 2024

Sprint reports are a crucial part of the software development process. They help in gaining visibility into the team’s progress, how much work is completed, and the remaining tasks.

While there are many tools available for sprint reports, the JIRA sprint report stands out to be the most reliable one. Thousands of development teams use it on a day-to-day basis. However, as the industry shifts towards continuous improvement, JIRA’s limitations may impact outcomes.

So, what can be the right alternative for sprint reports? And what factors to be weighed when choosing a sprint reports tool?

Importance of analyzing sprint reports

Sprints are the core of agile and scrum frameworks. It represents defined periods for completing and reviewing specific work.

Sprint allows developers to focus on pushing out small, incremental changes over large sweeping changes. Note that, they aren’t meant to address every technical issue or wishlist improvement. It lets the team members outline the most important issues and how to address them during the sprint.

Analyzing progress through sprint reports is crucial for several reasons:


Analyzing sprint reports ensures transparency among the team members. It includes an entire scrum or agile team that has a clear and shared view of work being done and pending tasks. There is no duplication of work since everything is visible to them.

Higher quality work

Sprint reports allow software development teams to have a clear understanding and requirements about their work. This allows them to focus on prioritized tasks first, fix bottlenecks in the early stages and develop the right solutions for the problems. For engineering leaders, these reports give them valuable insights into their performance and progress.

Higher productivity

Sprint reports eliminate unnecessary work and overcommitment for the team members. This allows them to allocate time more efficiently to the core tasks and let them discuss potential issues, risks and dependencies which further encourages continuous improvement. Hence, increasing the developers’ productivity and efficiency.

Optimize workflow

The sprint reports give team members a visual representation of how work is flowing through the system. It allows them to identify slowdowns or blockers and take corrective actions. Moreover, it allows them to make adjustments to their processes and workflow and prioritize tasks based on importance and dependencies to maximize efficiency.

JIRA sprint reports tick all of the benefits stated above. Here’s more to JIRA sprint reports:

JIRA sprint reports

Out of many sprint reporting software, JIRA Sprint Report stands out to be the out-of-the-box solution that is being used by many software development organizations. It is a great way to analyze team progress, keep everyone on track, and complete the projects on time.

You can easily create simple reports from the range of reports that can be generated from the scrum board:

Projects > Reports > Sprint report

There are many types of JIRA reports available for sprint analysis for agile teams. Some of them are:

  • Sprint burndown charts: Burndown chart measures daily completed work, monitors the total work to be done, and sets intended deadlines.
  • Burnup charts: It displays a sprint’s completed work in relation to its total scope.
  • Velocity chart: Velocity chart shows a Scrum team’s average work completed per sprint.
  • Cumulative flow diagram: It visually represents a Kanban team’s project progress over time.
  • Control chart: It maps the Cycle Time or Lead Time of each issue over a specified period.

JIRA sprint reports are built into JIRA software, convenient and are easy to use. It helps developers understand the sprint goals, organize and coordinate their work and retrospect their performance.

However, few major problems make it difficult for the team members to rely solely on these reports.

What’s missing in JIRA sprint reports?

Measures through story points

JIRA sprint reports measure progress predominantly via story points. For teams who are not working with story points, JIRA reports aren’t of any use. Moreover, it sidelines other potential metrics as well. This makes it challenging to understand team velocities and get the complete picture.

Can be misinterpreted in different ways

Another limitation is that the team has to read between the lines since it presents the raw data to team members. This doesn’t give accurate insights of what truly happening in the organization. Rather every individual can come with slightly different conclusions and can be misunderstood and misinterpreted in different ways.

Limited capabilities

JIRA add-ons need installation and have a steep learning curve which may require training or technical expertise. They are also restricted to the JIRA system making it challenging to share with external stakeholders or clients.

So, what can be done instead? Either the JIRA sprint report can be supplemented with another tool or a better alternative that considers all of its limitations. The latter option proves to be the right option since a sprint dashboard that shows all the data and reports at a single place saves time and effort.

How does Typo leverage the sprint analysis reports?

Typo’s sprint analysis is a valuable tool for any team that is using an agile development methodology. It allows you to track and analyze your team’s progress throughout a sprint. It helps you gain visual insights into how much work has been completed, how much work is still in progress, and how much time is left in the sprint. This information can help you to identify any potential problems early on and take corrective action.

sprint analysis reports

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 are taking, how often they’re being blocked, and where bottlenecks are occurring. This information can help you identify areas for improvement and make sure your team is on track to meet their goals.

It is easy to use and can be integrated with existing Git and Jira/Linear/Clickup workflows.

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.

How is it calculated?

Typo considers all the issues in the sprint and categorizes them based on their current status category, using JIRA status category mapping. It shows three major categories by default:

  • Open
  • In Progress
  • Done

These can be configured as per your custom processes. In the case of a closed sprint, Typo only shows the breakup of work on a ‘Completed’ & ‘Not Completed’ basis.

Work breakup

Work breakup represents the percentage breakdown of issue tickets in the current sprint according to their issue type or labels. This helps in understanding the kind of work being picked in the current sprint and plan accordingly.

How is it calculated?

Typo considers all the issue tickets in the selected sprint and sums them up based on their issue type.

Work  progress

Team velocity

Team Velocity represents the average number of completed issue tickets or story points across each sprint.

How is it calculated?

Typo calculates Team Velocity for each sprint in two ways :

  • For Issue Tickets: Typo calculates the sum of all the issue tickets completed in the sprint
  • For Story Points: Typo calculates the sum of story Points for all the issue tickets completed in the sprint

To calculate the average velocity, the total number of completed issue tickets or story points are divided by the total number of allocated issue tickets or story points for each sprint.

Team velocity

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.

Once the sprint is marked as ‘Closed’, it starts reflecting the count of Issue tickets/Story points that were not completed and were moved to later sprints as ‘Carry Over’.

How is it calculated?

Typo calculates the Developer Workload by considering all the issue tickets/story points assigned to each developer in the selected sprint and identifying the ones that have been marked as ‘Done’/’Completed’. Typo categorizes these issues based on their current workflow status that can be configured as per your custom processes.

The assignee of a ticket is considered in either of the two ways as a default:

  • The developer assigned to the ticket at the time it was moved to ‘In Progress’
  • Any custom field that represents the developer of that ticket

This logic is also configurable as per your custom processes.

Developer workload

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.

How is it calculated?

For all the ‘Done’/’Completed’ tickets in a sprint, Typo measures the time spent by each ticket to transition from ‘In Progress’ state to ‘Completion’ state.

By default, Typo considers 24 hours in a day and 7 day work week. This can be configured as per your custom processes.

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.

Typo’s sprint analysis tool monitors it to quantify its impact on the team’s workload and deliverables.

Issue cycle time


Sprint analysis tool is important for sprint planning, optimizing team performance and project outcomes in agile environments. By offering comprehensive insights into progress and task management, it empowers teams to focus on sprint goals, make informed decisions and drive continuous improvement.

To learn more about this tool, visit our website!

Top Software Development Analytics Tools (2024)

The demand for software development analytics tools is on the rise. The organizations aren’t just focusing on outcomes now, they want to get in-depth insights into teams’ health and progress. These tools measure the effectiveness and productivity of the team by turning data into actionable insights.

There are many software development analytics platforms available in the market. We’ve listed out the top 6 tools that you can choose from:

What are Software Development Analytics Tools?

Also known as an engineering management platform. These tools help engineering leaders and CTOs track team progress and health by combining various developer performance metrics, at a single place.

These software engineering analytics tools help gain visibility into the time spent on tasks, predict the time taken to complete the task, and report bugs and issues at an early stage. Hence, allowing organizations to make informed decisions, improve performance, and stay on schedule.

Why are Software Development Analytics Tools Necessary?

The software development industry is evolving. Engineering teams must stay updated with industry trends and best practices to deliver high-quality software to end-users. While meeting deadlines remains a crucial measure of a team’s performance and progress, it’s no longer the sole focus. Today, considerations extend to developers’ well-being and productivity that were usually overlooked earlier.

The organizations aren’t relying solely on DORA metrics now. They are combining it with other engineering metrics as well such as code churn, PR size, rework rate, and more to get in-depth insights into developers’ experience and performance. These software analytics tools consider both qualitative and quantitative aspects to evaluate developer success and gauge their burnout levels. This holistic approach enables engineering leaders to pinpoint bottlenecks, make informed decisions, and foster continuous improvement within their organizations.

Best Software Development Analytics Tools

Below are the top 6 software development analytics tools available in the market:


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.


  • Offers customized DORA metrics and other engineering metrics that can be configured in a single dashboard.
  • Includes effective sprint analysis feature that tracks and analyzes the team’s progress throughout a sprint.
  • Provides 360 views of the developer experience i.e. captures qualitative insights and provides an in-depth view of the real issues.
  • Offers engineering benchmark to compare the team’s results across industries.
  • User-friendly interface.


Jellyfish is a GIT-tracking tool that tracks metrics by aligning engineering insights with business properties. It gives a complete insight into the product from Github and Jira which further helps to decide what business value it provides. It also shows the status of every pull request and commits on the team. As a result, it provides full visibility into how engineering work fits in with your business objectives. Jellyfish can also be integrated with Bitbucket, Gitlab, and Google Sheets. However, it lacks user configuration for creating custom supports and UI can be tricky initially.


  • Offers multiple perspectives on resource allocation.
  • Real-time visibility into engineering organization and team progress.
  • Provides you access to benchmarking data on engineering metrics such as lead time and MTTR against industry standards and best practices.
  • Includes DevOps metrics for continuous delivery.
  • Transforms data into reports and insights for both management and leadership.


Swarmia is a well-known engineering analytics platform that gives engineering leaders and teams visibility across three crucial areas: Business outcomes, developer productivity, and developer experience. Its automation capabilities and SOC 2 certification increase the speed of the tasks without compromising on the product’s quality or developers’ well-being. Swarmia can be integrated with tools such as source code hosting, issue trackers, and chat systems. However, Swarmia lacks integrated benchmarks, making it challenging to gauge metrics against industry standards.


  • Its investment balance feature gives insights into the purpose of each action and money spent by the company on each category.
  • Offers user-friendly dashboard.
  • Its work agreement features include 20+ work agreements that are used by the industry’s top-performing teams.
  • Tracks healthy software engineering measures such as DORA metrics.
  • Reliable and prompt in-app customer support.

Swarmia raises a $7m Seed round led by Alven only 12 months after its  inception – Alven


LinearB is a real-time performance analysis tool that measures GIT data and meets business goals. It breaks different tasks into unique categories to refine reports and track individual or team performance. Besides this, LinearB can be integrated with Slack, JIRA, and popular CI/CD tools into testing and deployment metrics which helps to monitor the team’s progress in real-time. It also points out automatable tasks to the engineering teams that help in saving time and resources. The downside of LinearB is there are limited features to support SPACE framework metrics and individual performance insights.


  • Tracks DORA metrics and collects data from other tools to give holistic insights.
  • Offers reliable customer support.
  • Includes engineering benchmarks to stay ahead of the trends and keep up with industry standards.
  • Includes playbooks, guides, and presentation templates to present data and take actionable steps.
  • Includes project delivery forecasting to stay on schedule and communicate project delivery status updates.


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


  • Provides automated insights on metrics related to bug fixes, velocity, and more.
  • Easy to digest.
  • Allow engineering leaders to see data from different perspectives.
  • Creates custom goals, targets, or alerts.
  • Offers budgeting reports for engineering leaders.

Code Climate Velocity

Code climate velocity is an analytics platform that uses repos to synthesize data and provide in-depth visibility into code quality, code coverage, and security vulnerabilities. It analyses data from GIT repositories and then compresses it into real-time analytics. This tool supports both JIRA and GIT integration, Moreover, it can identify files that are frequently modified, and have poor coverage or maintenance issues. The drawback of Code Climate Velocity is that it includes non-standard metrics such as impact and traceability, they may not align intuitively with standard KPIs or OKRs.


  • Offers customized dashboards and trends.
  • Compiles data on developer velocity and code quality.
  • Provides technical debt assessment and style check in every pull request.
  • Provides views to each individual i.e. from day-to-day tasks to long progress.
  • Includes real-time risk alerts.

Announcing Velocity 2.0: The Most Powerful Platform… | Code Climate

Benefits of Software Development Analytics Tools

If you’re still in a dilemma about why you should consider software analytics tools for your organization, below are a few benefits you can reflect on:

Streamline Delivery Process

These tools offer data-driven insights that can help developers identify areas of improvement and fix them in the early stages. Moreover, these analytics tools allow teams to automate repetitive tasks. Hence, helping in reducing cycle time and ensuring consistent, error-free delivery.

Quality Assurance

Software development analytics tools continuously monitor and analyze development metrics and fix bottlenecks as early as possible. These tools can also forecast future quality based on historical data. As a result, allowing teams to deliver more reliable and stable software products and services.

Transparency and Accountability

These tools include dashboards and insights that provide stakeholders visibility into project progress, performance metrics, and team contributions. It helps in coordinating work and promoting transparency. Hence, fostering accountability among team members and encouraging collaboration towards common goals.

Lower Costs

These analytics tools have automated packages too. This allows the team to cut costs and focus on high-value projects. These analytics platforms also take note of areas of improvement and developers’ needs. Hence, helping in making informed decisions and getting the best out of this investment.

How to Pick the Right Software Development Analytics Tools?

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


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.


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.


Software development analytics tools play a crucial role in project pipelines and measuring and maximizing developers’ productivity. It allows engineering managers to gain visibility into the team’s performance through user-friendly dashboards and reports.

Select analytics tools that align with your team’s needs and specifications. Make sure they seamlessly integrate with your existing and forthcoming tech tools.

While we’ve curated the top six tools in the market, take the time to conduct thorough research before making a purchase.

All the best! :) 

A Guide to Static Code Analysis

The software development field is constantly evolving. Software must adhere to coding and compliance standards, should deploy on time, and be delivered to end-users quickly.

And in all these cases, mistakes are the last option for the software engineering team. Otherwise, they have to put in their energy and effort again and again.

This is how static code analysis comes to your rescue. They help development teams that are under pressure and decrease constant stress and worries.

Let’s learn more about static code analysis and its benefits:

What is Static Code Analysis?

Static code analysis is an effective method to examine source code before executing it. It is used by software developers and quality assurance teams. It identifies potential issues, vulnerabilities, and errors and also checks whether the coding style adheres to the coding rules and guidelines of MISRA and ISO 26262.

The word ‘Static’ states that it analyses and tests applications without executing them or compromising the production systems.

Static Code Analysis vs. Dynamic Code Analysis

The major difference between static code analysis and Dynamic code analysis is that the former identifies issues before you run the program. In other words, it occurs in a non-runtime environment between the time you create and the performance unit testing.

Dynamic testing identifies issues after you run the program i.e. during unit testing. It is effective for finding subtle defects and vulnerabilities as it looks at code’s interactions with other servers, databases, and services. Dynamic code analysis catches issues that might be missed during static analysis.

Note that, the static and dynamic analysis shouldn’t be used as an alternative to each other. Development teams must optimize both and combine both methods to get effective results.

How does Static Code Analysis Work?

Static code analysis is done in the creation phase. Static code analyzer checks whether the code adheres to coding standards and best practices.

The first step is making source code files or specific codebases available to static analysis tools. Then, the compiler scans the source code and makes the program source code translate from human readability to machine code. It further breaks code into smaller pieces known as tokens.

The next stage is parsing. The tokens are taken and sequenced in a way that makes sense according to the programming language which further means using and organizing them into a structure known as Abstract Syntax Tree.

Static Code Analysis Techniques

Data and Control Analysis

It helps in tracking the flow of data through the code to address potential issues such as uninitialized variables, null pointers, and data race conditions.

Control flow analysis helps to identify bugs like infinite loops and unreachable code.

Code Quality Analysis

It assesses the overall quality of code by examining factors like complexity, maintainability, and potential design flaws. It provides insights into potential areas of improvement that lead to more efficient and maintainable code.

Memory Leak Detection

Memory management that is improper can lead to memory leaks and decrease performance. It can identify areas of code that cause memory leaks. Hence, assisting developers to prevent resource leaks and enhancing application stability.

Benefits of Static Code Analysis

Helps to Identify Problems in the Early Stages

Effective static code analysis can detect potential issues early in the development cycle. It can catch bugs and vulnerabilities earlier that may otherwise go unnoticed until runtime. Hence, lowering the chances that crucial errors will go to the production stage leads to preventing developers from costly and time-consuming debugging efforts later.

Increases Productivity

Static code analysis reduces the manual and repetitive efforts that are required for code inspection. As a result, it frees developers time to focus more on creative and complex tasks. This not only enhances developers productivity but also streamlines the development cycle process.

Code Consistency and Compliance

Static code analysis enforces coding protocols, ensuring development teams follow a unified coding style, coding standards, and best practices. Hence, increasing the code readability, understandability, and maintainability. Moreover, static code analysis also enforces security standards and compliance by scanning code for potential vulnerabilities.

Streamlines Code Refinement

With the help of static code analysis, developers can spend more time on new code and less time on existing code as they don’t have to perform a manual code review. Static code analysis identifies and alerts users to problematic code and finds vulnerabilities even in the most remote and unattended parts of the code.

Increases Visibility

Static code analysis provides insights and reports on the overall health of code. This also helps in performing high-level analysis. Hence, spotting and fixing errors early, understanding code complexity and maintainability, and whether they adhere to industry coding standards and best practices.

Limitations of Static Code Analysis

Not Comprehensive in Nature

Static code analysis tools have scope limitations since they can only identify issues without executing the code. Consequently, performance, security, logical vulnerabilities, and misconfigurations that might be found during execution cannot be detected through them.

False Positive/Negative Results

Static code analysis can sometimes produce false positive/negative results. False negative occurs when vulnerabilities are discovered but not reported by the tool. Similarly, a false positive arises when new vulnerabilities in an external environment are uncovered or it has no runtime knowledge. In both cases, it leads to additional time and effort.

Lack of Context

Static code analysis may miss the broader architectural and functional aspects of the code being analyzed. It can lead to false positive/negative results, as mentioned above, and also miss problematic or genuine issues due to a lack of understanding of the code’s intended behavior and usage context.

Use of AI in Static Code Analysis

AI-powered static code analysis tools leverage artificial intelligence and machine learning to find and catch security vulnerabilities early in the application development life cycle. These AI tools can scan applications with far greater precision and accuracy than traditional queries and rule sets.

  • AI static analysis tools are powerful ways to scan code faster and more efficiently. Hence, making it easier to find vulnerabilities in more complex applications.
  • AI tools comprehend the context in which code is written. This makes it easier to discern false positives and negatives and provides more accurate analysis.
  • One major benefit of AI static code analysis tools is that it continuously learns from the code. It then further analyses and improves their accuracy over time as they encounter new issues.
  • These AI-powered tools can analyze historical code changes, and bug reports and perform data to predict potential issues or areas of code that could be prone to defects.
  • AI tools can automate the code review process by analyzing code changes, pull requests, or commits in real-time.

How to implement AI-powered static analysis tools?

  • Select the AI-based static analysis tool that aligns with the project’s programming languages, needs, and requirements.
  • Integrate it into development workflow i.e. setting up with VCS, IDEs, or CI/CD pipelines.
  • Train these tools using supervised learning techniques or labeled datasets. Make sure to train it related to specific codebase and project requirements.
  • Deploy them into production and monitor their performance and effectiveness over time. Ensure it aligns with the coding standards and best practices. Don’t forget to gather feedback from your developers.
  • Don’t forget to combine AI tools with human judgment to assess the tool’s recommendation and make informed decisions.

How Typo Leverage AI Analysis and Static Code Analysis?

Typo’s automated code review tool not only enables developers to merge clean, secure, high-quality code, faster. It lets developers catch issues related to maintainability, readability, and potential bugs and can detect code smells. It auto-analyses your codebase and pulls requests to find issues and auto-generates fixes before you merge to master.

Typo’s Auto-Fix feature leverages GPT 3.5 Pro to generate line-by-line code snippets where the issue is detected in the codebase. This means less time reviewing and more time for important tasks. As a result, making the whole process faster and smoother.

Issue detection by Typo

Autofixing the codebase with an option to directly create a Pull Request

Key Features

Supports Top 10+ Languages

Typo supports a variety of programming languages, including popular ones like C++, JS, Python, and Ruby, ensuring ease of use for developers working across diverse projects.

Fix every Code Issue

Typo understands the context of your code and quickly finds and fixes any issues accurately. Hence, empowering developers to work on software projects seamlessly and efficiently.

Efficient Code Optimization

Typo uses optimized practices and built-in methods spanning multiple languages. Hence, reducing code complexity and ensuring thorough quality assurance throughout the development process.

Professional Coding Standards

Typo standardizes code and reduces the risk of a security breach.

What is Code Complexity?

Code complexity is almost unavoidable in modern software development. High code complexity, when not tackled on time, leads to an increase in bugs, and technical debt, and negatively impacts the performance.

Let’s dive in further to explore the concept of cognitive complexity in software.

What is code complexity?

Code complexity refers to how difficult it is to understand, modify, and maintain the software codebase. It is influenced by various factors such as lines of code, code structure, number of dependencies, and algorithmic complexity.

Code complexity exists at multiple levels including the system architecture level, within individual modules or single code blocks.

The more the code complexity, the more complex a piece of code is. Hence, developers use it to make efforts to minimize it wherever possible. By managing code complexity, developers can reduce costs, improve software quality, and provide a better user experience.

Why does measuring code complexity matter?

Reduce bugs

In complex code, it becomes difficult to identify the root cause of bugs. Hence, making debugging a more arduous job. These changes can further have unintended consequences due to unforeseen interactions with other parts of the system. By measuring code complexity, developers can particularly complex identity areas that they can further simplify to reduce the number of bugs and improve the overall reliability of the software.

Increase collaboration

Managing code complexity increases collaboration between team members. Identifying areas of code that are particularly complex requires additional expertise. Hence, enhancing the shared understanding of code by reviewing, refactoring, or redesigning these areas to improve code maintainability and readability.

Better testing

High code complexity presents various challenges for testing such as increased test case complexity and reduced test coverage. Code complexity metrics help testers assess the adequacy of test coverage. It allows them to indicate areas of the code that may require thorough testing and validation. Hence, they can focus on high code complexity areas first and then move on to lower complexity areas.

Enhance performance

Complex code can also impact performance as complex algorithms and data structures can lead to slower execution times and excessive memory consumption. It can further hinder software performance in the long run. Managing code complexity encourages adherence to best practices for writing clean and efficient code. Hence, enhancing the performance of their software systems and delivering better-performing applications to end-users.

How does code complexity increase?

Poor readability and clarity

High code readability leads to an increase in code quality. However, when the code is complex, it lacks readability. This further increases the cognitive load of the developers and slows down the software development process.

The overly complex code is less modular and reusable which hinders the code clarity and maintenance.

Lack of code documentation

The main purpose of documentation is to help engineers work together to build a product and have clear requirements of what needs to be done. The unavailability of documentation may make developers’ work difficult since they have to revisit tasks, undefined tasks, and code overlapping and duplications.

Problematic architectural decisions

Architectural decisions dictate the way the software is written, how to improve it, tested against, and much more. When such decisions are not well documented or communicated effectively, it may lead to misunderstandings and inconsistency in implementation. Moreover, when the architectural decisions are not scalable, it may make the codebases difficult to extend and maintain as the system grows.

High coupling

Coupling refers to the connection between one piece of code to another. However, it is to be noted that they shouldn’t be highly dependent on each other. Otherwise, it leads to high coupling. It increases the interdependence between modules which makes the system more complex and difficult to understand. Moreover, it also makes the code difficult to isolate and test them independently.

Metrics to measure code complexity

Cyclomatic complexity

Cyclomatic complexity was developed by Thomas J. Mccabe in 1976. It is a crucial metric that determines the given piece of code complexity. It measures the number of linearly independent paths through a program’s source code. It is suggested cyclomatic complexity must be less than 10 for most cases. More than 10 means the need for refactoring the code.

To effectively implement this formula in software testing, it is crucial to initially represent the source code as a control flow graph (CFG). The CFG is a directed graph comprising nodes, each representing a basic block or a sequence of non-branching statements, and edges denoting the control flow between these blocks.

Cyclomatic complexity

Once the CFG for the source code is established, cyclomatic complexity can be calculated using one of the three approaches:

  • Basic cyclomatic complexity formula: Cyclomatic Complexity = E – N + 2P, where E corresponds to edges, N to nodes, and P to connected components.
  • Counting decision points: Cyclomatic Complexity = Number of decision points + 1.
  • Summing up predicate nodes: Cyclomatic Complexity = Sum of all predicate nodes + 1.

In each approach, an integer value is computed, indicating the number of unique pathways through the code. This value not only signifies the difficulty for developers to understand but also affects testers’ ability to ensure optimal performance of the application or system.

Higher values suggest greater complexity and reduced comprehensibility, while lower numbers imply a more straightforward, easy-to-follow structure.

The primary components of a program’s CFG are:

  • Nodes: Individual commands or statements.
  • Edges: Connections between nodes.
  • Connected components: Segments of the graph that are isolated from one another.

For instance, let’s consider the following simple function:

def simple_function(x):

   if x > 0:

       print(“X is positive”)


       print(“X is not positive”)

In this scenario:

E = 2 (number of edges)

N = 3 (number of nodes)

P = 1 (single connected component)

Using the formula, the cyclomatic complexity is calculated as follows: CC = 2 – 3 + 2*1 = 1

Therefore, the cyclomatic complexity of this function is 1, indicating very low complexity.

This metric comes in many built-in code editors including VS code, linters (FlakeS and jslinter), and IDEs (Intellij).

Cognitive complexity

Sonar developed a cognitive complexity metric that evaluates the understandability and readability of the source code. It considers the cognitive effort required by humans to understand it. It is measured by assigning weights to various program constructs and their nesting levels.

The cognitive complexity metric helps in identifying code sections and complex parts such as nested loops or if statements that might be challenging for developers to understand. It may further lead to potential maintenance issues in the future.

Low cognitive complexity means it is easier to read and change the code, leading to better-quality software.

Halstead volume

Halstead volume metric was developed by Maurice Howard Halstead in 1977. It analyzes the code’s structure and vocabulary to gauge its complexities.

The formula of Halstead volume:

N * log 2(n)

Where, N = Program length = N1 + N2 (Total number of operators + Total number of operands)

n = Program vocabulary = n1 + n2 (Number of operators + number of operands)

The Halstead volume considers the number of operators and operands and focuses on the size of the implementation of the module or algorithm.

Rework ratio

The rework ratio measures the amount of rework or corrective work done on a project to the total effort expended. It offers insights into the quality and efficiency of the development process.

The formula of the Rework ratio:

Total effort / Effort on rework * 100

Where, Total effort = Cumulative effort invested in the entire project

Effort on rework = Time and resources spent on fixing defects, addressing issues, or making changes after the initial dev phase

While rework is a normal process. However, a high rate of rework is considered to be a problem. It indicates that the code is complex, prone to errors, and potential for defects in the codebase.

Maintainability index

This metric measures the score of how easy it is to maintain code. The maintainability index is a combination of 4 metrics – Cyclomatic complexity, Halstead volume, LOC, and depth of inheritance. Hence, giving an overall picture of complexity.

The formula of the maintainability index:

171 – 5.2 * ln(V) – 0.23 * (G) – 16.2 * ln(LOC)

The higher the score, the higher the level of maintainability.

0-9 = Very low level of maintainability

10-19 = Low level of maintainability

20-29 = Moderate level of maintainability

30-100 = Good level of maintainability

This metric determines the potential challenges and costs associated with maintaining and evolving a given software system.

Lines of executable code or lines of source code

It is the easiest way to calculate and purely look at the number of LOCs. LOC includes instructions, statements, and expressions however, typically excludes comments and blank lines.

Counting lines of executable code is a basic measure of program size and can be used to estimate developers’ effort and maintenance requirements. However, it is to be noted that it alone doesn’t provide a complete picture of code quality or complexity.

How to reduce code complexity?

Create clear requirements

The requirements should be clearly defined and well-documented. A clear roadmap should be established to keep projects on track and prevent feature creep and unnecessary complexities.

It helps in building a solid foundation for developers and maintains the project’s focus and clarity. The requirements must ensure that the developers understand what needs to be built reducing the likelihood of misinterpretation.

Prioritize modular design

Break down software into smaller, self-contained modules. Each module must have a single responsibility i.e. focus on specific functions to make it easier to understand, develop, and maintain the code.

It is a powerful technique to manage complex code as well as encourages code reusability and readability.


Refactor continuously to eliminate redundancy, improve code readability and clarity, and adhere to best practices. It also helps streamline complex code by breaking down it into smaller, more manageable components.

Through refactoring, the development team can identify and remove redundant code such as dead code, duplicate code, or unnecessary branches to reduce the code complexity and enhance overall software quality.

Code reviews

Code reviews help maintain code quality and avoid code complexity. It identifies areas of code that may be difficult to understand or maintain later. Moreover, peer reviews provide valuable feedback and in-depth insights regarding the same.

There are many code review tools available in the market. They include automated checks for common issues such as syntax errors, code style violations, and potential bugs and enforce coding standards and best practices. This also saves time and effort and makes the code review process smooth and easy.

How Typo can improve your code review process?

Typo’s automated code review tool not only enables developers to catch issues related to 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. This means less time reviewing and more time for important tasks. It keeps the 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
  • Standardizes code and reduces the risk of a security breach
  • Provides automated debugging with detailed explanations
 code review process


Understanding and addressing code complexity is key to ensuring code quality and software reliability. By recognizing its causes and adopting strategies to reduce them, development teams can mitigate code complexity and enhance code maintainability, understandability, and readability.


Decoding Dev Mini Hoop Basketball

There is no ‘One Size approach’ in the software development industry. Combining creative ways with technical processes is the best way to solve problems.

While it seems exciting, there is one drawback as well. There are a lot of disagreements between developers due to differences in ideas and solutions. Communication is the key for most cases, but this isn’t feasible every time. There are times when developers can’t come to a general agreement.

This is when the HOOP (Having opposite opinions and picking solutions) system works best for the team.

But, before we dive deeper into this topic, let’s first know what the Mini hoop basketball game is about:

What is a Mini Basketball Hoop game?

Simply put, it is a smaller version of basketball that can be played indoors. It includes a smaller ball and hoop mounted on a wall or door.

A mini basketball hoop is a fun way to practice basketball skills and is usually enjoyed by people of all ages.

Mini Basketball Hoop game

Why Dev Mini Hoop Basketball game is beneficial for developers?

Below are a few ways how this game can positively impact developers in conflict-resolving and strengthening relationships with other team members:

Team bonding

This game creates a casual and enjoyable environment that strengthens team bonds, improving collaboration during work hours.

Enhanced focus

When developers take short breaks for a game, it helps prevent burnout and maintains high concentration levels during work hours. It leads to more efficient problem-solving and coding.

Conflict resolution skills

Developers practice conflict resolution when such differences arise in the game. As a result, they can apply these skills in the workplace.

Positive work environment

Indoor basketball hoop game contributes to a positive work environment as they instill a sense of fun and camaraderie. Hence, it positively impacts morale and motivation.

Official rules of Dev Mini Hoop Basketball game

Here's a step-by-step breakdown of the official rules for dev mini-hoop basketball:

Step 1

Start with Player 1, then proceed sequentially through players 2, 3, etc. Each player takes a shot from a spot of their choice.

Step 2

If the player before you makes a shot, make your shot exactly from the same spot. If you miss, you receive a strike.

Step 3

After a miss, the next player starts a new round from a different spot. If you make the shot, the next player replicates it from the same spot. If missed, they receive a strike.

Three strikes rule

Once a player hits the three-strike mark, they are out.

The game continues until there is a winner.

Time duration

The game usually concludes in about 10 minutes, if the whole team participates.


Dev Mini Hoop Basketball game is a fun way to resolve conflicts and strengthen relationships with other team members. Try it out with your team now!


Best CI/CD tools (2024)

Continuous integration/Continuous delivery (CI/CD) is positively impacting software development teams. It is becoming a common agile practice that is widely been adopted by organizations around the world. 

Hence, for the same, it is advisable to have good CI/CD tools to leverage the team’s current workflow and build a reliable CI/CD pipeline. Further, it automates the development process and lowers the delivery time to the end-users. 

There are an overflowing number of CI/CD tools available in the market right now. Thus, we have listed the top 14 tools to know about in 2024. But, before we move forward, understand these two distinct phases: Continuous Integration and Continuous Delivery: 

What is CI/CD?

Continuous Integration 

CI refers to the practices that drive the software development team to automatically and frequently integrate code changes into a shared source code repository. It helps in speeding up the process of building, packaging, and testing the applications. Although automated testing is not strictly part of CI, it is usually implied.

With this methodology, the team members can check whether the application is broken whenever new commits are integrated into the new branch. It allows them to catch and fix quality issues early and get quick feedback. 

This ensures that the software products are released to the end-users as quickly as possible (Every week, every day, or multiple times a day - As per the organization) and can create more features that provide value to them. 

Continuous Delivery

The CD begins when the continuous integration ends. 

It is an approach that allows teams to package software and deploy it into the production environment. It includes staging, testing, and deployment of CI code. 

It assures that the application is updated continuously with the latest code changes and that new features are delivered to the end users quickly. Hence, it helps to reduce the time to market and of higher quality.

Moreover, continuous delivery minimizes downtime due to the removal of manual steps and human errors. 

Benefits of CI/CD 

Reduce time to market 

CI/CD pipeline helps in building and delivering software to end-users at a rapid pace. It allows the development team to launch new features faster, implement deployment strategy, and collect feedback to incorporate promptly in the upcoming update.

Measure progress

CI/CD pipeline offers regular updates on the products and a set of metrics that include building, testing, coverage, and more. The release cycles are short and targeted and maintenance is done during non-business hours saving the entire team valuable time.

Quick feedback cycle

CI/CD pipeline gives real-time feedback on code quality, test results, and deployment status. It provides timely feedback to work more efficiently, identify issues earlier, gather actionable insights, and make iterative improvements.

Collaboration and communication

CI/CD pipeline encourages collaboration between developers, testers, and operation teams to reduce bottlenecks and facilitate communication. Through this, the team can communicate effectively about test results and take the desired action.

Improve quality and reliability

CI/CD pipeline enforces a rigorous testing process and conducts automated tests at every pipeline stage. The code changes are thoroughly tested and validated to reduce the bugs or regressions in software.

Best CI/CD tools 

GitLab CI/CD

It is a software development platform for managing different aspects of the software development lifecycle. With its cloud-based CI and deployment service, this tool allows developers to trigger builds, run, tests, and deploy code with each commit or push. 

GitLab CI/CD also assures that all code deployed to production adheres to all code standards and best practices. 

Key features

  • Direct connection with Kubernetes for efficient application deployment and scaling
  • Automatic security scans for compliance and vulnerability checks
  • Seamlessly integrate with third-party platforms including Slack, Asana, ClickUp, Notion, and Trello 
  • Real-time managing collaborative projects and tracking progress 
  • View, create, and manage codes and project data through branching tools 

GitHub Actions

GitHub Actions is a comparatively new tool for performing CI/CD. It automates, customizes, and executes software development workflows right in the repository.

GitHub Actions can also be paired with packages to simplify package management. It creates custom SDLC workflows in the GitHub repository directly and supports event-based triggers for automated build, test, and deployment. 

Key features

  • Fully integrates with GitHub
  • Built-in secret store
  • Include multiple CI templates or can create one
  • Live log feature offers insights into real-time workflow run
  • Support matrix build i.e. runs the same job with multiple configurations  


Jenkins is the first CI/CD tool that provides thousands of plugins to support building and deploying projects. It is an open source as well as a self-hosted automated server in which the central build and continuous integration take place. This tool can also be turned into a continuous delivery platform for any project. 

It is usually an all-rounder choice for the modern development environment.  

Key features

  • Simple and easy UI
  • Seamlessly integrate with major cloud platforms such as Azure, Google Cloud, Amazon web services, Slack, and Digital Ocean
  • Jenkin’s feature ‘Master-Slave Architecture’ helps in efficient resource usage
  • Real-time testing and reporting
  • 1500+ plugins and run by a high-paced community 

Circle CI

Circle CI is a CI/CD tool that is certified with FebRamp and SOC Type II compliant. It helps in achieving CI/CD in open-source and large-scale projects. It streamlines the DevOps process and automates builds across multiple environments. 

Circle CI provides two host offerings: 

  • Circle CI server - It lets CircleCI jobs on your own cloud instances hosted on AWS, Azure, Google Cloud, or your physical machines. 
  • Circle CI cloud: It includes cloud execution environments that involve Docker, Linux virtual machines, macOS, Windows, Arm, and GPU and can be spun up on-demand for every job in your workflow.

Key features

  • Circle CI insights track the status of projects and keep tabs on build processes 
  • Its feature ‘Parallel testing’ helps in running tests in parallel across different executors
  • Allow a single process per project
  • Reusable snippets of code ‘CircleCI Orbs’ help in automating repetitive processes
  • Provide ways to troubleshoot problems and inspect things such as directory path, log files, and running processes

Introduction to the CircleCI Web App - CircleCI

Bitbucket pipelines

An integrated CI/CD tool that is integrated into Bitbucket. It automates code from test to production and lets developers track how pipelines are moving forward at each step. 

Bitbucket pipelines ensure that code has no merge conflicts, accidental code deletions, or broken tests. Cloud containers are generated for every activity on Bitbucket that can be used to run commands with all the benefits of brand-new system configurations. 

Key features

  • Easy setup and configuration
  • Seamlessly integrate with Atlassian’s Bitbucket repositories
  • Define and manage different environments such as staging and production
  • Easily track the status of the codebase
  • Bitbucket domain is available for all static websites (which are hosted on the company cloud) 


A CI/CD tool that helps in building and deploying different types of projects on GitHub and Bitbucket. It runs in a Java environment and supports .Net and open-stack projects. 

TeamCity offers flexibility for all types of development workflow and practices. It archives or backs up all modifications errors and builds for future use. 

Key features

  • Identify unresponsive builds 
  • Offer comprehensive documentation
  • Run parallel builds simultaneously on different environments
  • Provide a live dashboard with build progress, statistics, and history 
  • Analysis code and offers quick feedback on potential issues; without affecting the main build 


Semaphore is a CI/CD platform with a pull-request-based development workflow. Through this platform, developers can automate build, test, and deploy software projects with the continuous feedback loop. 

Semaphore is available on a wide range of platforms such as Linux, MacOS, and Android. This tool can help in everything i.e. simple sequential builds to multi-stage parallel pipelines. 

Key features

  • Semaphore’s feature ‘Booster’ lowers the test suite run time through automatic parallelization of builds for Ruby projects
  • Its CLI is used for inspecting logs and debugging failures
  • Test reports provide a clear view of the team’s test suite 
  • Support Docker-based applications 
  • Seamlessly integrate with SCM tools including Github, Bitbucket, and Subversion 

Azure DevOps 

Azure DevOps by Microsoft combines continuous integration and continuous delivery pipeline to Azure. It includes self-hosted and cloud-hosted CI/CD models for Windows, Linux, and MacOS. 

It builds, tests, and deploys applications to the transferred location. The transferred locations include multiple target environments such as containers, virtual machines, or any cloud platform. 

Key features

  • Azure DevOps’ Agile boards help in agile planning and team visualization
  • Its feature ‘Test management’ allows the team to plan, create, and execute application tests
  • Seamlessly integrate with any GIT repository and different programming languages
  • Follow industry standards of YAML syntax
  • Accept any repository for package management 


Bamboo is a CI/CD server by Atlassian that helps software development teams automate the process of building, testing, and deploying code changes. It covers building and functional testing versions, tagging releases, and deploying and activating new versions on productions. 

This streamlines software development and includes a feedback loop to make stable releases of software applications. 

Key features

  • Create complex build pipelines with multiple stages and tasks
  • Support up to 100 remote build agents
  • Provide detailed reports and notifications
  • Run batches of tests in parallel
  • Support Docker and container applications

Bamboo: Continuous Integration & Deployment | Atlassian


An open-source CI/CD tool that is a Python-based twisted framework. It automates complex testing and deployment processes. With its decentralized and configurable architect, it allows development teams to define and build pipelines using scripts based on Python. 

Buildbot are usually for those who need deep customizability and have particular requirements in their CI/CD workflows. 

Key features

  • Run builds on various slave platforms
  • Easy setup and installation process
  • Support Docker images
  • Real-time monitoring and logging
  • Support several version control systems including GIT, CVS, and Subversion, and virtualization platforms such as Vmware and Virtual box

Travis CI

Travis CI primarily focuses on GitHub users. It provides different host offerings for open-source communities and enterprises that propose to use this platform on their private cloud. 

Travis CI is a simple and powerful tool that lets development teams sign up, link favorite repositories, and build and test applications. It checks the reliability and quality of code changes before integrating them into the production codebase. 

Key features

  • Follow a proprietary YAML syntax
  • Support multiple languages including Android, C##, C++, Python and Javascript
  • Live build views for monitoring GitHub projects
  • Seamlessly integrate with common cloud repositories including GitHub, GitLab, Kubernetes, and Azure
  • Support ‘Matrix build’ i.e. runs the same builds against multiple configurations and environment


Codefresh is a modern CI/CD tool that is built on the foundation of GitOps and Argo. It is Kubernetes-based and comes with two host offerings: Cloud and On-premise variants.

It provides a unique, container-based pipeline for a faster and more efficient build process. Codefresh offers a secure way to trigger builds, run tests, and deploy code to targeted locations. 

Key features

  • Codefresh's centralized dashboard offers insight into deployment at scale as well as security and support for the development team 
  • Support Docker images
  • Seamlessly integrate with well-known tools such as Bitbucket, GitHub, GitLab, and Slack
  • Improved visibility into the build process
  • Support build parallelism 


Buddy is a CI/CD platform that builds, tests, and deploys websites and applications quickly. It includes two host offerings: On-premise and public cloud variants. It is best suited for developers, QA experts, and designers. 

Buddy can not only integrate with Docker and Kubernetes, but also with blockchain technology. It gives the team direct deployment access to public repositories including GitHub. 

Key features

  • Simple and intuitive UI
  • Support Docker images 
  • Real-time progress and logs
  • Provide build actions for .Net, Angular, Ruby, Python, and Cypress
  • Available for free with 120 pipelines per month 
My Experience with Buddy CI - Continuous Integration - Dev Tips


Harness is the first CI/CD platform to leverage AI. It is a SaaS platform that builds, tests, deploys, and verifies on demand. 

Harness is a self-sufficient CI tool and is container-native so all extensions are standardized and builds are isolated. Moreover, it sets up only one pipeline for the entire log. 

Key features

  • Provide Developer-first experience
  • Auto-generate all deployment scripts; built-in or custom-based
  • Seamlessly integrate with observability platforms including Datadog and App dynamics
  • Quicken metrics, dashboards, and reporting 
  • Follow version control, codified approach to define and manage deployment pipelines

How Typo integrate with CI/CD tools? 

Typo seamlessly integrates with your CI/CD tools and offers comprehensive insights into your deployment process through key metrics such as change failure rate, time to build, and deployment frequency. 

It also delivers a detailed overview of the workflows within the CI/CD environment. Hence, enhances visibility and facilitates a thorough understanding of the entire development and deployment pipeline.

How to choose the right CI/CD tools? 

Prioritize your requirements

The CI/CD tool should best align with the needs and goals of the team and organization. In terms of features, understand what is important according to the specific requirements, project, and goals.

Development workflow 

The CI/CD tool should integrate smoothly into the developer workflow without requiring many customized scripts or plugins. The tool shouldn’t create friction or impose constraints on the testing framework and environment.

Security features

The CI/CD tool should include access control, code analysis, vulnerability scanning, and encryption. It should adhere to industry best practices and prevent malicious software from stealing source code.

Integrates with other tools

They should integrate with the existing setup and other important tools that are used daily. Also, the CI/CD tool should be integrated with the underlying language used for codebase and compiler chains.

Feedback and analysis

The tool should provide comprehensive feedback on multiple levels. It includes error messages, bug fixes, and infrastructure design. Besides this, the tool should notify of build features, test failures, or any other issues that need to be addressed.


The CI/CD tools mentioned above are the most popular ones in the market. Make sure you do your extensive research as well before choosing any particular tool. 

All the best! 


A Guide to Cognitive Complexity in Software

The journey of the software industry is full of challenges and innovations.

Cognitive complexity is one such aspect of software development. It takes into consideration how readable and understandable is the code for humans.

Let’s dig in further to explore the concept of cognitive complexity in software.

What is Cognitive Complexity?

Cognitive complexity was already a concept in psychology, however, it is now used in the tech industry too. It is a level of difficulty in understanding a given piece of code which could be a function, class, or issue.

A non-understandable code is a dead code.

Cognitive complexity is an important implication for code quality and maintainability. The more complexity of the code, the higher the chances of bugs and errors during modifications.  This can lower the developer productivity which further slows down the development process.

Factors that Influence Cognitive Complexity

Control Flow

Nested loops, deeply nested conditionals, and intricate branching logic can result in difficulty in understanding the code.

Function Length

Long functions or methods with multiple responses increase the cognitive load of the developers which makes it harder to understand the code. On the other hand, smaller, focused functions are generally easy to understand.

Code Structure

How the code is organized and structured directly affects how easily a developer can understand and navigate it. A well-structured code can make software easier to debug and maintain.

Usage of Libraries

When external libraries are integrated with complex APIs, it can introduce cognitive complexity, when not used judiciously.


Documentation acts as a bridge between the code and the software development team's understanding of it. Insufficient or poorly written documentation can result in high cognitive complexity.

Levels of Cognitive Complexity

Low Complexity Level

In this scenario, the code is relatively simple and easy to understand. The code adheres to the coding standards, follows best practices, and no unnecessary complexities are included. A few examples are Simple algorithms, straightforward functions, and well-structured classes.

Moderate Complexity Level

The code is slightly more complex and may require further efforts to understand and modify it. While it includes some areas of complexity that can be addressed but still manageable. For example, Function with multiple levels of nested loops and moderately complex algorithm.

High Complexity Level

At a high complexity level, the code is highly complex and difficult to understand. This makes the code more prone to errors and bugs and difficult to maintain and modify. This further increases the cognitive load of the developers. Complex algorithms with multiple layers of recursion and classes with a high number of interconnected methods are some examples.

Causes of Cognitive Complexity

Poor Architectural Decisions

Too much coupling between modules or poor separations of concerns are some of the wrong architectural decisions that can take place. Inadequate or intricate architectural choices can lead to higher cognitive complexity in software. This can further contribute to technical debt which can result in spending more time fixing issues and directly impact the system’s performance.

Lack of Knowledge and Experience

There may be many instances when developers are unfamiliar with technologies or have insufficient understanding of the industry for which software is developed. This can result in high cognitive complexity as there is a lack of knowledge regarding the development process.

Another instance could be when the software engineering team struggles with making sound architecture decisions or doesn’t follow coding guidelines.

Large Functions or Classes

Although large pieces of code including classes, functions, or modules aren’t necessarily complex. However, their increase in length may be a cause of high cognitive complexity.

In other words, more code = higher chances of cognitive complexity. It is because they are more prone to bugs and fixing issues. It can also increase the cognitive load of the developers since they have to comprehend large functions which will be more time-consuming.

Legacy Code

Aging or poorly maintained code can be challenging for the software engineering team to understand, update, or extend. This is because these codebases are usually outdated or aren’t documented properly. Moreover, they may also lack security features and protocols that make them more susceptible to security vulnerabilities and breaches. Outdated code can also pose integrating challenges.

High Essential Complexity

Essential complexity is a type of complexity that is intrinsic to the domain the developers are working on. It is an inherent difficulty of a problem software is trying to solve, regardless of how the problem is implemented or represented. This makes the underlying problem harder to grasp as the developers have to resort to heavy abstractions and intricate patterns. Hence, resulting in high cognitive complexity.

Unclear Naming Conventions and Comments

When the names in the code are deduced from their purpose and role or don’t provide clarity, it hinders the smooth navigation of the code. But that’s not all! Comments that are riddled with abbreviations, jargon, or incomplete also don’t provide clarity and add an unnecessary layer of mental effort for the development team to understand it.

Different Ways to Measure Cognitive Complexity

Pull Request Size

This metric calculates the average code changes (In lines of code) of a PR. The larger the size, the higher the chances of complex changes.

Screenshot 2024-05-12 at 12.15.55 AM.png

Cyclomatic Complexity

Cyclomatic complexity measures the number of linearly independent paths through a function or module. Higher cyclomatic complexity indicates the investigation of potentially challenging code sections.

Review Depth

It calculates the average number of comments per PR review. Review depth highlights the quality of the review and how thorough reviews are done and helps in identifying potentially complex sections before they get merged into the codebase.

Code Churn

Code churn doesn’t directly measure cognitive complexity. But, it tracks the number of times a code segment is modified. This suggests potential complexity due to differences in understanding or frequent adaption.

Nesting Complexity

This metric measures the depth of nested structures within code including loops and conditionals. The higher the nesting complexity, the harder it is to understand the code. Nesting complexity helps in identifying areas that are needed for simplification and refactoring.

Halstead Complexity Measures

It analyzes various aspects of code including operators and operands. This helps in estimating cognitive efforts and offers an overall complexity score. However, this metric doesn’t directly map to human understanding.

Use Static Analysis Tools

Static analysis tools such as Sonarqube take a unique approach to measuring cognitive complexity compared to many other static analysis tools. It incorporates various factors to provide a real assessment of the difficulty of the code such as control flow complexity, code smells, and human assessment. Based on all these factors, a cognitive complexity score is calculated for each function and class in the codebase.

How to Reduce Cognitive Complexity?


Apply refactoring techniques such as extracting methods or simplifying complex logic to improve code structure and clarity.

Follow Coding Standards and Best Practices

Adhere to coding principles such as KISS (Keep it short and simple) and DRY (Don’t repeat yourself) to increase the overall quality of code and reduce cognitive complexity.

Use Static Analysis Tools

As mentioned above, Static analysis tools are a great way to identify potentially complex functions and code smells that contribute to cognitive load. Through cognitive complexity score, developers can get to know the readability and maintainability of their code.

Encourage Clear Communication and Collaboration

By fostering an open communication culture, teammates can discuss code designs and complexity with each other. Moreover, reviewing and refactoring code together helps in maintaining clarity and consistency.

Typo’s automated code tool not only enables developers to catch issues related to 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. This means less time reviewing and more time for important tasks. It keeps the 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
  • Standardizes code and reduces the risk of a security breach
  • Provides automated debugging with detailed explanations


Understanding and addressing cognitive complexity is key to ensuring code quality and developer efficiency. By recognizing its causes and adopting strategies to reduce them, development teams can mitigate cognitive complexity and streamline the development process.

Top 10 best practices for code review

Code review is all about improving the code quality. However, it can be a nightmare for engineering managers and developers when not done correctly. They may experience several code review challenges and slow down the entire development process.Hence, following code review best practices to promote collaboration, improve code readability, and foster a positive team culture is crucial.

Types of Code Reviews

There are two types of code reviews: 1. Formal code review and 2. Lightweight code review.

Formal Code Review

As the name suggests, formal code reviews are based on a formal and structured process to find defects in code, specifications, and designs. It follows a set of established guidelines and involves multiple reviewers.

The most popular form of formal code review is Fagan Inspection. It consists of six steps: Planning, overview meeting, preparation, inspection meeting, casual analysis, reworking, and follow-up.

However, the downside of this type is that it is more time-consuming and resource-intensive than other types of code review.

Lightweight Code Review

Such a type of code review is commonly used by the development team and not testers. It is mostly followed when code review is not life-threatening. In other words, when reviewing a code doesn’t impact the software quality to a great extent.

There are four subtypes of lightweight code review:

Instant Code Review

This can also be known as pair programming. In this type, two developers work together on the same computer where one is writing code while the other is reviewing it in real time. Such a type is highly interactive and helps in knowledge sharing and spotting bugs.

Synchronous Code Review

In synchronous code review, the author produces the code themselves and asks the reviewer for feedback immediately when done with coding. The coder and reviewer then discuss and improve the code together. It involves direct communication and helps in keeping the discussion real around the code.

Asynchronous Code Review

While it is similar to synchronous code review, the only difference is that the code authors and reviewers don’t have to look at the code at the same moment. It is usually an ideal choice among developers because it allows flexibility and is beneficial for developers who work across various time zones.

Team Code Review

This type works for very specific situations. In this, different roles are assigned to the reviewers. It helps in more in-depth reviews and gives various perspectives. For team code reviews: code review tools, version control systems, and collaboration platforms are used.

Choose the correct code review type based on your team’s strengths and weaknesses as well as the factors unique to your organization.

Code Review Best Practices

Create a Code Review Checklist

Code review checklists include a predetermined set of questions and rules that the team will follow during the code review process. A few of the necessary quality checks include:

  • Readability and maintainability: This is the first criterion and cannot be overstated enough.
  • Uniform formatting: Whether the code with consistent indentation, spacing, and naming convention easy to understand? 
  • Testing and quality assurance: Whether it have meticulous testing and quality assurance processes? 
  • Boundary testing: Are we exploring extreme scenarios and boundary conditions to identify hidden problems? 
  • Security and performance: Are we ensuring security and performance in our source code?
  • Architectural integrity: Whether the code is scalable, sustainable, and has a solid architectural design?

Apart from this, answer three questions in your mind while reviewing the code. It includes:

  • Am I able to understand what the code does? 
  • Is the code functioning how I expect it to?
  • Is the code fulfilling the regular requirements? 

This allows you to know what to look for in a code review, streamline the code review, and focus on priorities.

Foster a Code Review Culture

The code review process must be an opportunity for growth and knowledge sharing rather than a critique of developers’ abilities.

To have effective code reviews, It is vital to create a culture of collaboration and learning. It includes encouraging pair programming so that developers can learn from each other and less experienced members can learn from their senior leaders.

You can establish code review guidelines that emphasize constructive feedback, respect, and empathy. Ensure that you communicate the goals of the code review and specify the roles and responsibilities of reviewers and authors of the code.

This allows the development team to know the purpose behind code review and take it as a way to improve their coding abilities and skills.

Provide Constructive Feedback

One of the code review practices is to provide feedback that is specific, honest, and actionable. Constructive feedback is important in building rapport with your software development team.

The feedback should point out the right direction rather than a confusion. It could be in the form of suggestions, highlighting potential issues, or pointing out blind spots.

Make sure that you explain the ‘Why’ behind your feedback so that it reduces the need for follow-ups and gives the necessary context. When writing comments, it should be written clearly and concisely.

This helps in improving the skills of software developers and producing better code which further results in a high-quality codebase.

Aim for Small, Incremental Changes

Instead of focusing on all the changes altogether, focus on a small section to examine all aspects thoroughly. It is advisable to break them into small, manageable chunks to identify potential issues and offer suggestions for improvement.

Focusing on a small section lets reviewers examine all aspects thoroughly (Use a code review checklist). Smaller the PRs, developers can understand code changes in a short amount of time and reviewers can provide more focused and detailed reviews. Each change is given the attention it deserves and easier to adhere to the style guide.

This helps in a deeper understanding of the code’s impact on the overall project.

Incorporate Code Review Goals and Capture Metrics

According to Goodhart’s law, “When a measure becomes a target, it ceases to be a good measure”.

To measure the effectiveness of code review, have a few tangible goals so that it gives a quantifiable picture of how your code is improving. Have a few metrics in mind to determine the efficiency of your review and analyze the impact of the change in the process.

You can use SMART criteria and start with external metrics to get the bigger picture of how your code quality is increasing. Other than this, below are a few internal key metrics that must be kept in mind:

  • Inspection rate: The speed at which code is reviewed.
  • Defect rate: It is measured by dividing the number of defects found by the number of hours spent on a code review.
  • Defect density: It is measured by dividing the defect count by thousands of lines of code. 

Besides this, you can use metrics-driven code review tools to decide in advance the goals and how to measure the effectiveness.

Don’t Rush your Review

As mentioned above, don’t review the code all at once. Keep these three things in mind:

  • Don’t review the code for longer than 60 minutes. 
  • Limit your review to 200-400 lines of code at once. 
  • Plan for 3 seconds of review time per line of code. 

This is because reviewing the code continuously can drop off the focus and attention to detail. This further makes it less effective and invites burnout.

Hence, conduct code review sessions often and in short sessions. Encourage few breaks in between and set boundaries otherwise, defects may go unnoticed and the purpose of the code review process remains unfulfilled.

Rotate Code Reviewers Regularly

Relying on the same code reviewers consistently is a common challenge that can cause burnout. This can negatively impact the software development process in the long run.

Hence, encourage a rotation approach i.e. different team members can participate in reviewing the code. This brings in various skill sets and experience levels which promotes cross learning and a well-rounded review process. It also provides different points of view to get better solutions and fewer blind spots.

With this approach, team members can be familiar with different parts of the codebase, avoid bias in the review process, and understand each other's coding styles.

Always Document Code Review Decisions

Documenting code review decisions is a great way to understand the overall effectiveness of the code review process. Ensure that you record and track the code review outcome for future reference. It is because this documentation makes it easier for those who may work on the codebase in the future.

It doesn’t matter if the review type is instant or synchronous.

Documentation provides insights into the reasoning behind certain choices, designs, and modifications. It helps in keeping historical records i.e. changes made over time, reasons for those changes, and any lessons learned during the review process. Besides this, it accelerates the onboarding process for new joiners.

As a result, documentation and tracking of the code review decisions encourage the continuous improvement culture within the development team.

Focus on Coding Standards, Not Personal Preference

Emphasizing coding standards promotes consistency, readability, maintainability, and overall code quality.

Personal preferences vary widely among developers. Hence, by focusing on coding standards, team members can limit subjective arguments and rather rely on documented agreed-upon code review guidelines. It helps in addressing potential issues early in the development process and ensures the codebase remains consistent over time.

Besides this, adhering to coding standards makes it easier to scale development efforts and add new features and components seamlessly.

Automate as Much as Possible

Code review is a vital process yet it can be time-consuming. Hence, automate what can be automated.

Use code review tools like Typo to help improve the code quality and increase the level of speed, precision, and consistency. This allows reviewers to take more time in giving valuable feedback, automate, track changes, and enable easy collaboration. It also ensures that the changes don’t break existing functionality and streamline the development process.

Typo - 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


If you prioritize the code review process, do follow the above-mentioned best practices. These code review best practices maximize the quality of the code, improve the team’s productivity, and streamline the development process.

Happy reviewing!

Common Mistakes to Avoid During Code Reviews

The code review process is vital to the software development life cycle. It helps improve code quality and minimizes technical debt by addressing potential issues in the early stages.

Due to its many advantages, many teams have adopted code review as an important practice. However, it can be a reason for frustration and disappointment too which can further damage the team atmosphere and slow down the entire process. Hence, the code review process should be done with the right approach and mindset.

In this blog post, we will delve into common mistakes that should be avoided while performing code reviews.

Benefits of Code Reviews

Helps in Optimizing Code for Better Performance

Performing code review helps in identifying areas of improvement in the initial stages. It also helps in code scalability i.e. whether the code can handle increased loads and user interactions efficiently. Besides this, it allows junior developers and interns to gain the right feedback and hone their coding skills. This, altogether, helps in code optimization.

Enhance Maintainability of the Code

Code reviews allow maintaining code easily even when the author is unavailable. It lets multiple people be aware of the code logic and functionality and allows them to follow consistent coding standards. The code review process also helps in identifying opportunities for refactoring and eliminating redundancy. It also acts as a quality gate to ensure that the code is consistent, clear, and well-documented.

Knowledge Sharing

The code review process provides mutual learning to both reviewers and developers. It not only allows them to gain insights but also to understand each other perspectives. For newbies, they get an idea of why certain things are done in a certain way. It includes the architecture of the application, naming conventions, conventions of structuring code within a class, and many more.

Coding Standards and Compliance

Performing code reviews helps in maintaining consistent coding styles and best practices across the organization. It includes formatting, code structure, naming conventions, and many more. Besides this, code review is often integrated with the dev workflow. Hence, it cannot be merged into the main code base without passing through the code review process.

Saves Time

While code review is a tedious task, it saves developers time in fixing bugs after the product’s release. A lack of a code review process can increase flaws and inconsistencies in code. It also increases the quality of code which are more maintainable and less prone to errors. Further, it streamlines the development process and reduces technical debt which saves significant time and effort to resolve later.

Common Mistakes to Avoid During the Code Review Process

Providing Vague Feedback

Code reviewers do provide feedback. Yet, most of the time they are neither clear nor actionable. This not only leads to delays and ambiguity but also slows down the entire development process.

For example, if the reviewer adds a comment ‘Please change it’ without giving any further guidance or suggestion. The code author may take it in many different ways. They may implement the same according to their understanding or sometimes they don’t have enough expertise to make changes.

Unfortunately, it is one of the most common mistakes made by the reviewers.


  • Always provide clear and actionable feedback to the developers. Never assume they will figure it out on their own. 
  • State what and where it is wrong. Be specific and explain the reason behind suggesting or pointing out a particular issue. 
  • Identify the concerns and suggest ideas or provide guidance that can help developers to implement them accordingly. 

These suggestions will allow code authors to understand the reviewer’s perspective and make necessary changes.

Skipping Tests

The review contains a variety of tests such as unit tests, integration tests, end-to-end tests, and many more. It gets difficult to review all of them which lets reviewers skim through them and jump straight to implementations and conclusions.

This not only eludes the code review process but also puts the entire project at risk. The reasons behind not reviewing the tests are many including time-constraint and not understanding the signs of robust testing and not prioritizing it.


  • Educate the development team on the importance of testing and its best practices. 
  • Review tests to understand its working and functioning. It eliminates the risk of bugs and errors in later stages. 
  • Use automated testing tools for quick feedback on code functioning and catching regressions. 

Skipping tests is a common mistake by reviewers. It is time-consuming for sure, but it comes bearing a lot of benefits too.

Reviewing Only New-Added Codes

Another common mistake is only reviewing changed lines of code. Code review is an ever-evolving process that goes through various phases of change.

Old lines are deleted accidentally or ignored because for obvious reasons can be troublemakers. Reviewing only newly added codes overlooks the interconnected nature of a codebase and results in missing specific details that can further, jeopardize the whole project.


  • Code should be considered as a whole, complete story that shouldn’t be broken down or read in parts.
  • Examine the complete set of changes to figure out how new code integrates with existing code. 
  • Foster collaboration and pair programming among team members to gain in-depth insights about the same and make the task easier. 

Always review existing and newly added codes together to evaluate how new changes might affect existing functionality.


A proper code review process needs both time and peace. The rushed review may result in poorly written code and hinder the process's efficiency. Reviewing code before the demo, release, or deadline are a few reasons behind rushed reviews.

During rush reviews, code reviewers read the code lines rather than reading the code through lines. It usually happens when reviewers are too familiar with the code. Hence, they examine by just skimming through the code.

It not only results in missing out on fine and subtle mistakes but also compromises coding standards and security vulnerabilities.


  • Establish reasonable review timeframes. In the case of large reviews, break down larger tasks into smaller, manageable chunks for focused and effective reviews. 
  • Distribute review responsibilities among team members to ensure fresh perspectives and avoid burnout. 
  • Use automated code review tools to help detect bugs and make the process easier. 

Rush reviews should be avoided at any cost. Use the suggestions to help in reviewing the code efficiently.

Not Going into Design and Architecture

It is the responsibility of the reviewer to examine the entire code - From design and language to mechanism and operations. However, most of the time, reviewers focus only on the functionality and operationality of the code. They do not go much into designing and architecture part.

It could either be due to limited time or a rush to meet deadlines. However, it may demand close consideration and observation to look into the design and architecture side to understand how it ties in with what’s already there.


  • Establish shared design principles and guidelines for reviewers and team members. Each of them must be aware of how design and architecture fit in the bigger picture. 
  • Consult with other reviewers and understand their different perspectives since each of them may catch different design considerations. 
  • Offer training and mentorship on recognizing and evolving architectural patterns. 

Focusing on design and architecture ensures a holistic assessment of the codebase, fostering long-term maintainability and alignment with overall project goals.

Performing Code Reviews Without a Checklist

A code review checklist is important while doing code reviews. Without the checklist, the process is directionless. Not only this, reviewers may unintentionally overlook vital elements, lack consistency, and miss certain aspects of code. Not using the checklist may confuse whether all the aspects are covered as well and key best practices, coding standards, and security considerations may be neglected.


  • A code review checklist is the most productive way to avoid mistakes. A checklist should include a list of common mistakes and important aspects such as security, business logic, and user access. 
  • The checklist can also include the specific expectations for team members and the form of review. 
  • A code review checklist can ensure an added layer of consistency and involves a structured approach. 

Behind effective code reviews is a checklist that involves every task that needs to be ticked off.

What shouldn’t be a part of the code reviews?

Cosmetic Concerns

A code review should not include cosmetic concerns; it will efficiently use time. Use a tool to manage these concerns, which can be predefined with well-defined coding style guides.

For further reference, here are some cosmetic concerns:

  • Indentation with spacing: The placement of opening brackets within the code clocks, the inclusion of spaces of keywords like “if,” “for, and others, or utilization of tabes and spaces. 
  • Naming conventions: Whether to choose between snake_case, camelCase, or PascalCase for the different variables and function names 
  • Formatting considerations: Decisions about introducing blank lines in scenarios to improve code readability 

Automated Testing

Functional flaws of the code should not be reviewed separately as this leads to loss of time and manual repetition. The reviewer can instead trust automated testing pipelines to carry out this task.

Automation over Manual Checks

Enforcing coding standards and generating review notifications should also be automated, as repetitive tasks enhance efficiency.

Personal Inclinations for Code Standards

As a code reviewer, base your reviews on the established team and organizational coding standards. Imposing the coding standards that reviewers personally follow should not serve as a baseline for the reviews.

Balancing the Code for Simplicity

Reviewing a code can sometimes lead to the practice of striving for perfection. Overanalyzing the code can lead to this. Instead, as a code reviewer, focus on improving readability and following the best practices.

Lack of follow-up actions

Another common mistake is that reviewers don’t follow up after reviewing. Following up is important to address feedback, implement changes, and resolve any issues identified.

The lack of follow-up actions is also because reviewers assume that identified issues will be resolved. In most cases it does, but still, they need to ensure that the issues are addressed as per the standard and in the correct way.

It leads to accountability gaps, and unclear expectations, and the problems may persist even after reviewing negatively impacting code quality.


  • Always track and document the follow-up actions clearly and systematically. 
  • Schedule follow-up meetings or check-ins to review the progress. 
  • Encourage open communication so that developers and junior team members can freely seek clarification and facilitate peer programming.

Lack of follow-up actions may lead to no improvements or outcomes. Hence, it is an important practice that needs to be followed in every organization.

Typo - 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


The code review process is an important aspect of the software development process. However, when not done correctly, it can negatively impact the project.

Follow the above-mentioned suggestions for the common mistakes to not let these few mistakes negatively impact the software quality.

Happy reviewing!


R&D Cost Capitalization for Engineering Teams

Research and Development (R&D) has become the hub of innovation and competitiveness in the dynamic world of modern business. A deliberate and perceptive strategy is required to successfully navigate the financial complexities of R&D expenses.

When done carefully, the process of capitalizing R&D expenses has the potential to produce significant benefits. In this blog, we dive into the cutting-edge R&D cost capitalization techniques that go beyond the obvious, offering practical advice to improve your financial management skills.

What is R&D Cost Capitalization?

Capitalizing R&D costs is a legitimate accounting method that involves categorizing software R&D expenses, such as FTE wages and software licenses, as investments rather than immediate expenditures. Put more straightforwardly, it means you're not merely spending money; instead, you're making an investment in the future of your company.

Capitalizing on R&D costs entails a smart transformation of expenditures into strategic assets supporting a company's financial structure beyond a simple transaction. While traditional methods follow Generally Accepted Accounting Principles (GAAP), it is wise to investigate advanced strategies.

One such strategy is activity-based costing, which establishes a clear connection between costs and particular R&D stages. This fine-grained understanding of cost allocation improves capitalization accuracy while maximizing resource allocation wisdom. Additionally, more accurate appraisals of R&D investments can be produced using contemporary valuation techniques suited to your sector's dynamics.

Software Costs that Qualify for Capitalization

This is to be noted that only some expenditures can be converted into assets. GAAP guidelines are explicit about what qualifies for cost capitalization in software development. R&D must adhere to specific conditions to be recognized as an asset on the balance sheet. These include:

Technology Should be Feasible

The capitalizable cost should be contributing to a tangible product or process.

Commitment to Finish

The firm’s commitment should evolve into a well-defined plan. The half-hearted endeavors should be eliminated.

Economic Possibilities

Projections for market entry and the product must yield financial returns in the future.

In software development costs, GAAP’s FASB Account Standard Codification ASC Topic 350 - Intangibles focuses on internal use only software eligible for capitalization:

  • Software developed for internal use only. If the company plans to sell the prototype, it becomes an expense.
  • Activities that are undertaken during the development stage - Testing, coding, and installation.
  • FTE compensation for engineers involved in the development during the specified period.

That being said, FASB Accounting Standards Codification (ASC) Topic 985 – Software addresses sellable software for external use. It covers:

  • Costs that are incurred in the technology feasibility stage.
  • Activities during the development stage, such as testing, coding, installation, independent consultations, product development, and FTE compensation.

Note that, costs related to initial planning and prototyping cannot be capitalized. Therefore, they are not exempted from tax calculations.

In R&D capitalization, tech companies typically capitalize on engineering compensation, product owners, third-party platforms, algorithms, cloud services, and development tools.
Although, In some cases, an organization's acquisition targets may also be capitalized and amortized.

Making Decisions Based on Insights Using Investment Balance Tools

Enhancing your understanding of R&D cost capitalization necessitates adopting techniques beyond quantitative data to offer a comprehensive view of your investments. These tools transform numerical data into tactical choices, emphasizing the critical importance of data-driven insights.

Adopt tools that are strengthened by advanced analytics and supported by artificial intelligence (AI) prowess to assess the prospects of each R&D project carefully. This thorough review enables the selection of initiatives with greater capitalization potential, ultimately optimizing the investment portfolio. Additionally, these technologies act as catalysts for resource allocation consistent with overarching strategic goals.

In Typo, you can use “Investment distribution” to allocate time, money, and effort across different work categories or projects for a given period of time. Investment distribution helps you optimize your resource allocation and drive your dev efforts towards areas of maximum business impact.

These insights can be used to evaluate project feasibility, resource requirements, and potential risks. You can allocate your engineering team better to drive maximum deliveries.

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Amortization Techniques for Comprehensive Financial Management

Effective amortization is the trajectory, while capitalization serves as the launchpad, defining intelligent financial management. For amortization goals, distinguishing between the various R&D components necessitates nothing less than painstaking thought.

Advanced techniques emphasize personalization by calibrating amortization periods to correspond to the lifespan of specific R&D assets. Shorter amortization periods are beckoned by ventures with higher risk profiles, reflecting the uncertainty they carry. Contrarily, endeavors that have predictable results last for a longer time. This customized method aligns costs with the measurable gains realized from each R&D project, improving the effectiveness of financial management.

Tailoring R&D Capitalization to Industry Dynamics

R&D cost capitalization should be tailored to the specific dynamics of each industry, taking into account the specifics of each sector. Combining agile approaches with capitalization strategies yields impressive returns in industries like technology, known for their creativity and flexibility.

Capitalization strategies dynamically alter when real-time R&D progress is tracked using agile frameworks like Scrum or Kanban. This realignment makes sure that the moving projects are depicted financially accurately. Your strategy adapts to the contextual limits of the business by using industry-specific performance measures, highlighting returns within those parameters.

A comprehensive Plan for Effective Financial Governance

Controlling the complexities of R&D financial management necessitates an ongoing voyage marked by the fusion of approaches, tools, and insights specific to the sector. Combining the methods presented here results in a solid framework that fosters creativity while maximizing financial success.

It is crucial to understand that the adaptability of advanced R&D cost capitalization defines it. Your journey is shaped by adapting techniques, being open to new ideas, and being skilled at navigating industry vagaries. This path promotes innovation and prosperity in the fiercely competitive world of contemporary business and grants mastery over R&D financials.


SDLC Best Practices for Software Development

A well-organized and systematic approach must be in place to guarantee the success of your software development initiatives. The Software Development Lifecycle (SDLC), which offers a structure for converting concepts into fully functional software, can help.

Adopting cutting-edge SDLC best practices that improve productivity, security, and overall project performance is essential in the cutthroat world of software development. The seven core best practices that are essential for achieving excellence in software development are covered in this guide. These practices ensure that your projects always receive the most optimal results. Let’s dive into the seven SDLC best practices.

Comprehensive Planning and Requirement Analysis

This is an essential step for development teams. A thorough planning and requirement analysis phase forms the basis of any successful software project.

Establish Definite Goals and Scope

Start by defining the scope and objectives of the project. Keep a thorough record of your expectations, limitations, and ambitions. This guarantees everyone is on the same page and lessens the possibility of scope creep.

Involve Stakeholders Early

Engage stakeholders right away. Understanding user wants and expectations greatly benefits from their feedback. Refinement of needs is assisted by ongoing input and engagement with stakeholders.

Conduct Market Research

Conduct thorough market research to support your demand analysis. Recognize the preferences of your target market and the amount of competition in the market. This information influences the direction and feature set of your project.

Create an Effective Project Plan

Make a thorough strategy that includes due dates, milestones, and resource allocation. Your team will be more effective if you have a defined strategy that serves as a road map so that each member is aware of their duties and obligations. Also, ensure that there is effective communication within the team so that everyone is aligned with the project plan.

Agile methods that are adaptable

Agile methodologies, which promote flexibility and teamwork, such as Scrum and Kanban, have revolutionized software development. In the agile model, the team members are the heartbeat of this whole process. It fosters an environment that embraces collaboration and adaptability.

Accept Iterative Development

Apply a strategy that enables continual development. Thanks to this process, agile team members can respond to shifting requirements and incrementally add value.

Interdepartmental Teams

Teams made up of developers, testers, designers, and stakeholders should be cross-functional. Collaboration across diverse skill sets guarantees faster progress and more thorough problem-solving.

Sprint Reviews

Implement regular sprint reviews during which the team displays its finished products to the stakeholders. The project will continue to align with shifting requirements because of this feedback loop.

Agile Tools

Use agile project management tools like Jira or Trello to aid in sprint planning, backlog management, and real-time collaboration. These tools enhance transparency and expedite agile processes.

Safety First Mentality

Security is vitally important in today's digital environment as a rise in security issues can result in negative consequences. Hence, adopting security best practices ensures prioritizing security measures and mitigating risks.

Threat Modeling

Early on in the development phase, a threat modeling step should be conducted, and you should approach potential security risks and weaknesses head-on. It helps in identifying and addressing security vulnerabilities before they can be exploited.

Continuous Security Testing

Integrate continuous security testing into the whole SDLC. Integrated components should include both manual penetration testing and automated security scanning. Security flaws must be found and fixed as soon as possible.

Follow Security Trends

Keep up with recent developments and security threats. Participate in security conferences, subscribe to security newsletters, and encourage your personnel to take security training frequently.

Security of Third-Party Dependencies

Analyze and protect any third-party libraries and parts used in your product. Leaving third-party code vulnerabilities unfixed can result in serious problems.

Streamlined Development and Deployment

For timely software delivery, effective development and deployment process is crucial. Not only this, software testing plays a crucial role in ensuring the quality and application of the software.

CI/CD Pipelines

Automate code testing, integration, and deployment with continuous integration/Continuous Deployment (CI/CD) pipelines. As a result, the release cycle is sped up, errors are decreased, and consistent software quality is guaranteed. Application security testing can be seamlessly integrated into CI/CD pipelines to mitigate security vulnerabilities during the testing phase.


Use orchestration with Kubernetes and tools like Docker to embrace containerization. Containers isolate dependencies, guaranteeing consistency throughout the development process.

Building Blocks as Code

To manage and deploy infrastructure programmatically, apply Infrastructure as Code (IaC) principles. Automating server provisioning with programs like Terraform and Ansible may ensure consistency and reproducibility.


A/B testing and feature flags are important components of your software development process. These methods enable you to gather user feedback, roll out new features to a select group of users, and base feature rollout choices on data.

High standards for testing and code quality

Software must follow stringent testing requirements and approved coding standards to be trusted.


Compliance with industry-specific regulations and standards is crucial, and adherence to these standards should be a priority so that the final product meets all necessary compliance criteria.

Code Reviews

To preserve code quality and encourage knowledge sharing, regular code reviews should be mandated. Use static code analysis tools to identify potential problems early.

Testing Automation

A large collection of automated tests should encompass unit, integration, and regression testing. Automating the process of making code modifications can prevent new problems from arising.

Code Quality Metrics

To monitor the evolution of your codebase over time, create metrics for code quality. The reliability, security, and maintainability of a piece of code can be determined using SonarQube and other technologies.

Load Testing

Use load testing as part of your testing process to ensure your application can manage the expected user loads. The next step is performance tuning after load testing. Performance optimization must be continuous to improve your application's responsiveness and resource efficiency.

Documentation and version control

For collaboration and knowledge preservation in software teams, efficient documentation and version control are essential.

Change Management

Use version control systems like Git to manage codebase changes methodically. Use branching approaches to create well-organized teams.

Living Documentation Techniques

Maintain up-to-date user manuals and technical documentation. These tools promote transparency while facilitating efficient maintenance and knowledge transfer.

Instantaneous Documentation

The use of "living documentation" techniques, which automatically treat documentation like code and generate it from source code comments, is something to consider. This guarantees that the documentation is current when the code is developed.

Git Workflow

Establish for your teams a clear Git workflow that considers code review procedures and branching models like GitFlow. Collaboration is streamlined by using consistent version control procedures.

Performance Enhancement and Ongoing Improvement

Long-term success depends on your software operating at its best and constantly improving.

Performance Testing

Testing should be integrated into your SDLC. To improve resource utilization, locate and fix bottlenecks. Assessments of scalability, load, and stress are essential.

Monitoring Following Deployment

To acquire insights into application performance implement real-time monitoring and logging as part of your deployment process. Proactive issue detection reduces the possibility of downtime and meets user expectations.

Feedback Loops for Users

Identify methods for gathering user input. User insights enable incremental improvements by adapting your product to changing user preferences.

Error Tracking and Reporting

Implement error tracking and reporting technologies to get more information about program crashes and errors. Maintaining a stable and dependable software system depends on promptly resolving these problems.

SDLC methodologies

Software development lifecycle methodologies are structured frameworks used by software development teams to navigate the SDLC.

There are various SDLC methodologies. Each has its own unique approach and set of principles. Check below:

Waterfall Model

According to this model, software development flows linearly through various phases: requirements, design, implementation, testing, deployment, and maintenance. There is no overlapping and any phase can only initiate when the previous one is complete.


Although, DevOps is not traditionally an SDLC methodology, but a set of practices that combines software development and IT operations. DevOps' objective is to shorten the software development lifecycle and enhance the relevance of the software based on users' feedback.

Agile Methodology

Although it has been mentioned above, Agile methodology breaks a project down into various cycles. Each of them passes through some or all SDLC phases. This methodology also incorporates users' feedback throughout the project.

Iterative Model

It is an early precursor to Agile and emphasizes iterative and incremental action. The iterative model is beneficial for large and complex applications.


An extension of the waterfall model, this model is named after its two key concepts: Validation and Verification. It involves testing and validation in each software development phase so that it is closely aligned with testing and quality assurance activities.

Software Excellence for Business Advancement

Technical expertise and process improvement are required on the route to mastering advanced SDLC best practices. These techniques can help firms develop secure, scalable, high-quality software solutions. Due to their originality, dependability, and efficiency, these solutions satisfy the requirements of the contemporary business environment.

If your company adopts best practices, it can position itself well for future growth and competitiveness. By taking software development processes to new heights, one can discover that superior software leads to superior business performance.


Crafting an Effective Code Review Checklist

Code reviews are the cornerstone of ensuring code quality, fostering a collaborative relationship between developers, and identifying potential code issues in the primitive stages.

To do this well and optimize the code review process, a code review checklist is essential. It can serve as an invaluable tool to streamline evaluations and guide developers.

Let’s explore what you should include in your code reviews and how to do it well.

A Blueprint for Effective Code Review Checklists

50% of the companies spend 2-5 hours weekly on code reviews. You can streamline this process with a checklist, and developers save time. Here are eight criteria for you to check for while conducting your code reviews with a code review tool or manually. It will help to ensure effective code reviews that optimize both time and code quality.

Readability and Maintainability

A complicated code is not helpful to anyone. Therefore, while reviewing code, you must ensure readability and maintainability. This is the first criterion and cannot be overstated enough.

Organized Code Composition

The code must be orchestrated into well-defined modules, functions, and classes. Each of them must carry a unique role in the bigger picture. You can employ naming conventions for each component to convey its purpose, ensuring code changes are easily understood and the purpose of the different components at a glance.

Uniform Formatting

A code with consistent indentation, spacing, and naming convention is easy to understand. To do this well, you should enforce a standard format that minimizes friction between team members who have their own coding styles. This will ensure a consistent code across the team.

Contextual Annotation

By adding in-line comments and documentation throughout the code, you will help explain the complex logic, algorithms, and business rules. Coders can use this opportunity to explain the ‘why’ behind the coding decisions and not only explain ‘how’ something is done. It adds context and makes the code-rich in information. When your codebase is understandable to the current team members and future developers who would handle it – you pave the way for effective collaboration and long-standing code. Hence, facilitating actionable feedback and smoother code change.

Architectural Integrity - Build the Code for Scalability and Sustainability

No building is secure without a solid foundation – the same logic applies to a codebase. The code reviewer has to check for scalability and sustainability, and a solid architectural design is imperative.


Partition of the code into logical layers encompassing presentation, business logic, and data storage. This modular structure enables easy code maintenance, updates, and debugging.

Design Patterns

In software development, design patterns are a valuable tool for addressing recurring challenges consistently and efficiently. Developers can use established patterns to avoid unnecessary work, focus on unique aspects of a problem, and ensure reliable and maintainable solutions. A pattern-based approach is especially crucial in large-scale projects, where consistency and efficiency are critical for success.

Testing and Quality Assurance

Code reviews have to ensure meticulous testing and quality assurance processes. This is done to maintain high test coverage and quality standards.

Coverage Assurance

When you test your code, it's essential to ensure that all crucial functionalities are accounted for and that your tests provide comprehensive coverage.

Boundary Testing

You should explore extreme scenarios and boundary conditions to identify hidden problems and ensure your code behaves as expected in all situations, meeting the highest quality standards.

Ensuring Security and Performance

Ensuring security and performance in your source code is crucial in the face of rising cyber threats and digital expansion, making valuable feedback a vital part of the process.


Scrutinize the user inputs that check for security vulnerabilities such as SQL injection. Check for the input of validation techniques to prevent malicious inputs that can compromise the application.


If the code performance becomes a bottleneck, your application will suffer. Code reviews should look at the possible bottlenecks and resource-intensive operations. You can utilize the profiling tools to identify them and look at the sections of the code that are possibly taking up more resources and could slow down the application.

When code reviews check security and performance well, your software becomes effective against potential threats.

Object-Oriented Analysis and Design Principles (OOAD)

OOAD principles offer the pathway for a robust and maintainable code. As a code reviewer, ensuring the code follows them is essential.

Single Responsibility Clarity

When reviewing code, aim for singular responsibilities. Look for clear and specific classes that aren't overloaded. Encourage developers to break down complex tasks into manageable chunks. This leads to code that's easy to read, debug, and maintain. Focus on guiding developers towards modular and comprehensible code to improve the quality of your reviews.

Substitution Dynamics

It's important to ensure that derived classes can seamlessly replace base classes without affecting consistency and adaptability. To ensure this, it's crucial to adhere to the Liskov Substitution Principle and verify that derived classes uphold the same contract as their base counterparts. This allows for greater flexibility and ease of use in your code.

Non-functional Requirements

Beyond mere functionality, non-functional requirements define a codebase's true mettle:

Code Transparency

While reviewing code, you should ensure the code is self-explanatory and digestible for all fellow developers. The code must have meaningful variable and function names, abstractions applied as needed, and without any unnecessary complications.

Debugging with Clarity

When it comes to debugging, you should carefully ensure the right logging is inserted. Check for log messages that offer context and information that can help identify any issues that may arise.

Configuration and Environment

A codebase should be adaptable to any environment as needed, and a code reviewer has to check for the same.

Externalized Configuration

A code reviewer should ensure the configuration values are not included within the code but are placed externally. This allows for easy modifications and ensures that configuration values are stored in environment variables or configuration files.

Cross-Platform Compatibility

A code should ideally perform well and consistently across diverse platforms. A reviewer must check if the code is compatible across operating systems, browsers, and devices.When the code can perform well under different environments, it improves its longevity and versatility.

Fostering Collaboration and Constructive Feedback

The final part of code reviewers is to ensure the process results in better collaboration and more learning for the coder.

Constructive Feedback

Good feedback helps the developer in their growth. It is filled with specific, actionable insights that empower developers to correct their coding process and enhance their work.

Knowledge Sharing

Code reviews should be knowledge-sharing platforms – it has to include sharing of insights, best practices, and innovative techniques for the overall developer of the team.

Code Review Best Practices

A code reviewer must ensure that certain best practices are followed to ensure effective code reviews and maintain clean code:

Evade any Hardcoding

Hard coding shouldn’t be a part of any code. Instead, it should be replaced by constants and configuration values that enhance adaptability. You should verify if the configuration values are centralized for easy updates and if error-prone redundancies are reduced.

Insightful Comments

The comments shared across the codebase must focus on problem-solving and help foster understanding among teammates.


Complicated if/else blocks and switch statements should be replaced by succinct, digestible frameworks. As a code reviewer, you can check if the repetitive logic is condensed into reusable functions that improve code maintainability and reduce cognitive load.

What Shouldn’t Be a Part of the Code Reviews?

Cosmetic Concerns

A code review should not include cosmetic concerns; it will efficiently use your time. Use a tool to manage these concerns, which can be predefined with well-defined coding style guides.

For further reference, here are some cosmetic concerns:

  • Indentation with spacing: The placement of opening brackets within the code clocks, the inclusion of spaces of keywords like “if,” “for, and others, or utilization of tabes and spaces.
  • Naming conventions: Whether to choose between snake_case, camelCase, or PascalCase for the different variables and function names 
  • Formatting considerations: Decisions about introducing blank lines in scenarios to improve code readability 

Automated Testing

Functional flaws of the code should not be reviewed separately as this leads to loss of time and manual repetition. The reviewer can instead trust automated testing pipelines to carry out this task.

Automation over Manual Checks

Enforcing coding standards and generating review notifications should also be automated, as repetitive tasks enhance efficiency.

Personal Inclinations for Code Standards

As a code reviewer, you should base your reviews on the established team and organizational coding standards. Imposing the coding standards that you personally follow should not serve as a baseline for the reviews.

Balancing the Code for Simplicity

Reviewing a code can sometimes lead to the practice of striving for perfection. Overanalyzing the code can lead to this. Instead, as a code reviewer, you can focus on improving readability and following the best practices.

Review Codes for Best Code Outcomes

The process of curating the best code review checklist lies in ensuring alignment of readability, architectural finesse, and coding best practices with quality assurance. Hence, promoting code consistency within development teams.

This enables reviewers to approve code that performs well, enhances the software, and helps the coder in their career path. This collaborative approach paves the way for learning and harmonious dynamics within the team.

Typo, an intelligent engineering platform, can help in identifying SDLC metrics. It can also help in detecting blind spots which can ensure improved code quality.


Establishing Effective Agile Team Working Agreements

Agile practices enable businesses with adaptability and help elevate their levels of collaboration and innovation. Especially when changing landscapes in tech, agile working models are a cornerstone for businesses in navigating through it all.

Therefore, agile team working agreements are crucial to successfully understand what fuels this collaboration. They serve as the blueprint for agile team members and enable teams to function in tandem.

In this blog, we discuss the importance of working agreements, best practices, and more.

What are Agile Teams?

Agile teams are a fundamental component of agile development methodologies. These are cross-functional teams of individuals responsible for executing agile projects.

The team size is usually ranging from 5 to 9 members. It is chosen deliberately to foster collaboration, effective communication, and flexibility. They are autonomous and self-organizing teams that prioritize customer needs and continuous improvement. Often guided by an agile coach, they can deliver incrementally and adapt to changing circumstances.

What are Agile Team Working Agreements?

Agile team working agreements are guidelines that outline how an agile team should operate. They dictate the norms of communication and decision-making processes and define quality benchmarks.

This team agreement facilitates a shared understanding and manages expectations, fostering a culture aligned with Agile values and team norms. This further enables collaboration across teams. In the B2B landscape, such collaboration is essential as intricate projects require several experts in cross-functional teams to work harmoniously together towards a shared goal.

Significance of Agile Team Agreements

Agile Team Working Agreements are crucial for defining specific requirements and rules for cooperation. Let's explore some further justifications for why they are vital:

  • Setting up clear norms and expectations for collaboration helps avoid miscommunications and conflicts within the team. Pair programming is a prime example of this. It helps in establishing a shared understanding of how developers will collaborate; hence, reducing potential misunderstandings.
  • You will foster a sense of ownership and accountability for the project's success by including every team member in the decision-making process. It will ensure transparency and trust among the team members as their input and opinions are valued.

Working agreements can aid in fostering openness and communication within the team. When everyone is on the same page on how to collaborate, productivity and efficiency rise.

Agile Team Working Agreements can encourage a culture of continuous improvement because team members can review and amend the agreement over time.

Crafting Effective Working Agreements

Working agreements should be a collaborative process to involve the entire team and get different perspectives. Here are some steps to follow:

Assemble the Team

Gather all team members; the scrum master, product owner, and all other stakeholders.

Have an Open Dialogue

Once you have the team, encourage everyone to share their thoughts and ideas about the team, the working styles, and the dynamics within the team. Ask them for areas of improvement and ensure the Scrum Master guides the conversation for a more streamlined flow in the meeting.

Identify the Challenges

During retrospectives, identify the challenges or issues from the previous sprints. Discuss how they propose to solve such challenges from coming up again through the working agreements.

Propose Solutions

Once you’ve heard the challenges and suggestions, propose the potential solutions you can implement through the working agreements and ensure your team is on board with them. These agreements must support the team‘s goals and improve collaboration.

Document the Agreements

Write the agreed-upon working agreements clearly in a document. Make sure the document is accessible to all the team members physically or as a digital resource.

Key Components of Effective Working Agreements

To create effective working agreements, you must also know what goes into it and the different aspects to cover. Here are five components to be included in the agreement.

Defined Behaviors

Outline how you would like the decorum of the team members to be – this will ensure the culture of the team and company is consistently upheld. Nurture a culture of active listening, collaborative ideation, and commitment to their work. Ensure professionalism is mentioned.

Technology Use

Once you have the team, encourage everyone to share their thoughts and ideas about the team, the working styles, and the dynamics within the team. Ask them for areas of improvement and ensure the Scrum Master guides the conversation for a more streamlined flow in the meeting.


Establish communication guidelines, including but not limited to preferred channels, frequencies, and etiquette, to ensure smooth conversations. Clear communication is the linchpin of successful product building and thus makes it an essential component.

Effective Meetings and Working Hours

Set the tone for meetings with structured agendas, time management, and participation guidelines that enable productive discussions. Additionally, defining meeting times and duration helps synchronize schedules better.

Decision-making approach

Clear decision-making is crucial in B2B projects with multiple stakeholders. Transparency is critical to avoiding misunderstandings and ensuring everyone's needs and team goals are met.

Conflict Resolution Strategy

To maintain a healthy work environment, encourage open communication and respectful disagreement. When conflicts arise, approach them constructively and find a solution that benefits all parties. Consider bringing in a neutral third party or establishing clear guidelines for conflict resolution. This helps complex B2B collaborations thrive.

Best practices for Efficient Working Arrangements

Start Easy

It's essential to start with core guidelines that everyone can agree upon when drafting working agreements. These agreements can be improved as a team grows older, laying a solid foundation for difficult B2B cooperation. Team members can concentrate on what's essential and prevent confusion or misunderstandings by keeping things simple.

Inclusive Team Participation

Involving all team members in formulating the working agreements is crucial to ensuring everyone is committed to them. This strategy fosters a sense of ownership and promotes teamwork. When working on B2B initiatives, inclusivity provides a well-rounded viewpoint that can produce superior results.


To guarantee comprehension and consistency, a centralized document that is available to all team members must be maintained. This paperwork is very helpful in B2B partnerships because accountability is so important. Team members can operate more effectively and avoid misunderstandings by having a single source of truth.

Ongoing Assessment

Maintaining continued relevance requires routinely reviewing and revising agreements to reflect changing team dynamics. This agility is crucial in the constantly evolving B2B environment. Teams may maintain focus and ensure everyone is on the same page and working toward the same objectives by routinely reviewing agreements.

Onboarding New Participants

To guarantee seamless integration into the team's collaborative standards when new team members join a project, it's crucial to introduce them to the working agreements. Rapid onboarding is essential for B2B cooperation to keep the project moving forward. Teams can prevent delays and keep the project moving ahead by swiftly bringing new members up to speed.

Attributes of Effective Working Agreements

The following essential qualities should be taken into account to foster teamwork through working agreements:

Accessibility and Visibility

Be careful to display the agreements in a visible location in the workplace. This makes it easier to refer to established norms and align behaviors with them. Visible agreements provide constant reminders of the team's commitments. Feedback loops such as one-on-one meetings, and regular check-ins help ensure that these agreements are actively followed and adjusted, if needed.

Clarity and Simplicity

Create agreements that are clear-cut and simple to grasp. All team members are more likely to obey and abide by clear and simple guidelines. Simpler agreements reduce ambiguity and uncertainty, hence fostering a culture of continuous improvement.

Regular Reevaluation

Review and revise the agreements frequently to stay current with the changing dynamics of the team. The agreements' adaptability ensures that they will continue to be applicable and functional over time. Align it further with retrospective meetings; where teams can reflect on their processes and agreements as well as take note of blind spots.

Shared Commitment

Develop a sense of shared responsibility among team members to uphold the agreements they have made together. This shared commitment strengthens responsibility and respect for one another, ultimately encouraging collaboration.

Enforcing Working Agreements

Once you have created your working agreements, it is crucial to enforce them to see effective results.

Here are five strategies to enforce the working agreements.

Embrace Precision through Automation

Use automated tools to enforce the code-related aspects of working agreements. Automation ensures consistency reduces errors, and enhances efficiency in business-to-business projects.

Strengthen Commitment with Reviews and Retrospectives

Code reviews and retrospectives help reinforce the significance of teamwork agreements. These sessions support improvement. Serve as platforms for upholding established norms.

Encourage Peer Accountability

Foster a culture of peer accountability where team members engage in dialogues and provide feedback. This approach effectively integrates working agreements into day-to-day operations.

Regular Check-ins for Timely Corrections

Incorporate check-ins, stand-up meetings, or retrospective meetings to discuss progress and address challenges. These interactions offer opportunities to rectify any deviations from established working agreements.

Recognize Consistency with Rewards

Acknowledge and reward team members who consistently uphold working agreements. Publicly recognizing their dedication fosters a sense of pride. Further promotes an environment.

Teams can greatly enhance their dedication to working agreements. Establish an atmosphere that fosters project collaboration and success by prioritizing these strategies.

Cultivating Collaborative Brilliance

Collaboration plays a role in B2B software development. Agile Team Working Agreements are instrumental in promoting collaboration. This guide highlights the significance of these agreements, explains how to create them, and offers practices for their implementation.

By crafting these agreements, teams establish an understanding and set expectations, ultimately leading to success. As teams progress, these agreements evolve through retrospectives and real-life experiences, fostering excellence, innovation, and continued collaboration.

Story Points vs. Hours - Which is the Right Way?

For every project, whether delivering a product feature or fulfilling a customer request, you want to reach your goal efficiently. But that’s not always simple – choosing the right method can become stressful. Whether you want to track the tasks through story points or hours, you should fully understand both of them well.

Therefore in this blog, story points vs. hours, we help you decide.

What are the Story Points?

When it comes to Agile Software Development, accurately estimating the effort required for each task is crucial. To accomplish this, teams use Story Points, which are abstract units of measurement assigned to each project based on factors such as complexity, amount of work, risk, and uncertainty.

These points are represented by numerical values like 1, 2, 4, 8, and 16 or by terms like X-Small, Small, Medium, Large, and Extra-Large. They do not represent actual hours but rather serve as a way for Scrum teams to think abstractly and reduce the stress of estimation. By avoiding actual hour estimates, teams can focus on delivering customer value and adapting to changes that may occur during the project.

The Need for Estimating Work in Software Development

Why are Story Points Important?

When estimating the progress of a project, it's crucial to focus on the relative complexity of the work involved rather than just time. Story points help with this shift in perspective, providing a more accurate measure of progress.

By using this approach, collaboration and shared understanding among team members can be promoted, which allows for effective communication during estimation. Additionally, story points allow for adjustments and adaptability when dealing with changing requirements or uncertainties. By measuring historical velocity, they enable accurate planning and forecasting, encouraging velocity-based planning.

Overall, story points emphasize the team's collective effort rather than individual performance, providing feedback for continuous improvement.

Hours and Their Role in Traditional Project Management

Project management can involve various methodologies and estimating work in terms of hours. While this method can be effective for plan-driven projects with inflexible deadlines, it may not be suitable for projects that require adaptability and flexibility. For product companies, holding a project accountable has essential.

Why are Hours Important in Project Management?

Hours provide stakeholders with a clear understanding of the time required to complete a project and enable them to set realistic expectations for deadlines. This encourages effective planning and coordination of resources, allocation of workloads, and creation of project schedules and timelines to ensure everyone is on the same page.

One of the most significant advantages of using hours-based estimates is that they are easy to understand and track progress. It provides stakeholders with a clear understanding of how much work has been done and how much time remains. By multiplying the estimated hours by the hourly rate of resources involved, project costs can be estimated accurately. This simplifies billing procedures when charging clients or stakeholders based on the actual hours. It also facilitates the identification of discrepancies between the estimated and actual hours, enabling the project manager to adjust the resources' allocation accordingly.

Story Points and Hours - Where do They Lack?

Story Points

Estimating the time and effort required for a project can be daunting. The subjectivity of story points can make it challenging to compare and standardize estimates, leading to misunderstandings and misaligned expectations if not communicated clearly.

Furthermore, teams new to story points may face a learning curve in understanding the scale and aligning their estimations. The lack of a universal standard for story points can create confusion when working across different teams or organizations.Additionally, story points may be more abstract and less intuitive for stakeholders, making it difficult for them to grasp progress or make financial and timeline decisions based on points. It's important to ensure that all stakeholders understand the meaning and purpose of story points to ensure everything is understood.


Relying solely on hours may only sometimes be accurate, especially for complex or uncertain tasks where it's hard to predict the exact amount of time needed. This approach can also create a mindset of rushing through tasks, which can negatively affect quality and creativity.

Instead, promoting a collaborative team approach and avoiding emphasizing individual productivity can help teams excel better.

Additionally, hourly estimates may not account for uncertainties or changes in project scope, which can create challenges in managing unexpected events.

Lastly, sticking strictly to hours can limit flexibility and prevent the exploration of more efficient or innovative approaches, making it difficult to justify deviating from estimated hours.

Story Points vs. Hours - Choose What Works for your Organization

It can be daunting to decide what works best for your team, and you don’t solely have to rely on one solution most of the time - use a hybrid approach instead.

When trying to figure out what tasks to tackle first, using story points can be helpful. They give you a good idea of how complex a high-level user story or feature is, which can help your team decide how to allocate resources. They are great for getting a big-picture view of the project's scope.

However, using hours might be a better bet when you're working on more detailed tasks or tasks with specific time constraints. Estimating based on hours can give you a much more precise measure of how much effort something will take, which is important for creating detailed schedules and timelines. It can also help you figure out which tasks should come first and ensure you're meeting any deadlines that are outside your team's control. By using both methods as needed, you'll be able to plan and prioritize more effectively.


Best code quality tools (2024)

Coding is a fundamental aspect of software development. Since an increase in the number of complex and high-profile security software projects, coding is becoming an important part of digital transformation as well.

But, there is a lot more to coding than just writing code and executing it. The developers must know how to write high-quality and clean code and maintain code consistency. As it not only enhances the software but also contributes to a more efficient development process.

This is why code quality tools are here to your rescue. But, before we suggest you some code quality tools, let’s first understand what ‘Low-quality code’ is and what metrics need to be kept in mind.

How to define ‘Low-quality code’?

In simple words, low-quality code is like a poorly-written article.

An article that consists of grammatical errors and disorganized content which, unfortunately, fails to convey the information efficiently. Similarly, low-quality code is poorly structured and lacks adherence to coding best practices. Hence, fails to communicate logic and functions clearly.

This is why measuring code quality is important. The code quality tools consider the qualitative and quantitative metrics for reviewing the code.

Let’s take a look at the code metrics for code quality evolution below:

Qualitative metrics


The code’s ability to perform error-free operations whenever it runs.


A good-quality code is easy to maintain i.e. adding new features in less time with less effort.


The same code can be used for other functions and software.


The code is portable when it can run in different environments without any error.


A code is of good quality when a smaller number of tests are required to verify it.


When the code is easily read and understood.


The good-quality code should be clear enough to be easily understood by other developers.


A well-documented code is when a code is both readable and maintainable i.e. Enabling other developers to understand and use it without much time and effort.


A good quality code takes less time to build and is easy to debug.


The extensible code can incorporate future changes and growth.

Quantitative metrics

Weighted micro function points:

A soft sizing algorithm that breaks down your source code into various micro functions. The result is then interpolated into a single score.

Halstead complexity measures:

The set of measures to evaluate the computational complexity of a software program. More the complexity, the lower the code quality.

Cyclomatic complexity:

It measures the structural complexity of the code. It is computed using the control flow graph of the program.

5 best static code analysis tools

Static analysis code tools are software programs and scripts that analyze source or compiled code versions ensuring code quality and security.

Below are 5 best static code analysis tools you can try:


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



A well-known static code analysis tool that enables you to write safer and cleaner code. It is an open-source package that finds different types of bugs, vulnerabilities, and issues in the code.

  • Boasts comprehensive language support. A few of the popular ones are C#, Python, and Java. 
  • Permits integration with CI/CD pipelines with Azure DevOps server, Jenkins, and many more. 
  • Allows you to inspect the project’s code quality and security. 
  • Presents results in the form of rich reports. 


Veracode is another static analysis tool that offers fast scans and real-time feedback on your source code. It measures the software security posture of all your applications.

  • Can be integrated with more than 40 tools and APIs. 
  • Analyse code across over 100 programming languages. Some of them are PHP, JavaScript, and SQL. 
  • Secure your software without sacrificing speed. 
  • Allows you to easily track the analyses. 


Another great offering among static analysis tools that helps you check our code quality. It blocks merges of pull requests based on your quality rules and helps prevent critical issues from affecting your product.

  • Supported by more than 40+ languages. A few of them are C++, Go, and CoffeeScript. 
  • Lets you identify which codes are being covered by your test suite.
  • Provides suggestions for fixes. 
  • Allows customization of analysis with hundreds of rules available. 

How do I block merging pull requests using Codacy as a quality gate? -  Codacy docs


A well-known static analysis tool that focuses on managing and monitoring the quality of software projects. It enables you to automatically prioritize problematic snippets in the code and provide clear visualizations.

  • Supported by various popular programming languages. A few of them are Objective-C, Solidity, and C. 
  • Monitoring options include Quality checkpoints, Customer KPIs, and Custom quality checkpoints. 
  • Multi-vector diagnostic technology that helps in analyzing code. 
  • Helps in transparently managing and improving software quality.  

PVS Studio

PVS Studio is best known for detecting bugs and security weaknesses. It offers a digital reference guide for all analytic rules and analysis codes for errors, dead snippets, typos, and redundancy.

  • Monitors code quality for a variety of languages. A few of them are Visual Studio, C++, and C#. 
  • The analysis report is available in various formats. HTML, XML, and TeamCity to name a few. 
  • Can be easily integrated with various products. It includes Jenkins, SonarQube, Rider, and many more. 
  • Allows simple navigation through the code’s warnings. 

5 best dynamic code analysis tools

Dynamic code analysis tools enable you to analyze and test your applications during execution against possible vulnerabilities.

Choosing what tools fit your requirements could be a bit tricky. As these tools are language-specific and case-specific. You can pick the right tool from an open-source repository by Github based on your current situation.However, we have picked 5 popular dynamic code analysis tools that you can take a look at:


A real-time code coverage tool that provides insights for penetration testing activities.

  • Currently supports Java programs up to Java 11, and .NET Framework programs for CLR versions 2 and 4.
  • Presents coverage information visually; making it easy to understand which part of an application is covered and how much. 
  • Automatically detects coverage information while the tests are being conducted. 


A vulnerability scanner that checks whether the code follows best practices in security, performance, and reliability.

  • Specifically designed for Laravel PHP applications that combine SAST, DAST, IAST, and configuration analysis techniques to detect vulnerabilities.
  • Helps in detecting issues in the earlier stages. 
  • Presents issues with all information and documentation links required to resolve it. 

Enlightn: Boost your Laravel App's Performance & Security


An interactive tool that analyses un-instrumented ELF core files for leaks, memory growth, and corruption.

  • Used in automation to catch leaks before they are committed. 
  • Currently supports only glibc malloc.
  • Supplements a debugger by giving the status of various memory locations.


A framework for dynamic analysis of WebAssembly binaries.

  • Built on binary instrumentation. 
  • Offers an easy-to-use, high-level API that allows the implementation of heavyweight dynamic analyses that can monitor all low-level behaviour.


An instrumental framework that automatically detects many memory management and threading bugs.

  • Helps in building dynamic analysis tools for C/C++ programs.
  • Runs on UNIX systems such as Linux. 
  • Runs the binary on a CPU emulator with the appropriate.
  • Instrumentation for the selected analysis. 

5 best peer code review tools

Although static and dynamic code analysis tools are effective, they won’t catch everything. Since they aren’t aware of the business practices and functionality you are trying to implement.

This is when you need another developer from your organization. And this is possible with the peer code review tools. They not only help in making better code but better teams as well.

A few of the questions that another developer considers are:

  • Can I find any obvious logic errors in the code? 
  • Are all cases fully implemented based on the current requirements?
  • Are the new automated tests enough for the new code? Or do the existing ones need to be rewritten to account for changes in the code?
  • Does the new code abide by prevailing style guidelines?

Below are 5 best peer code review tools that you can use:

SmartBear Collaborator

A peer code and document review tool that enables a team to collaborate and produce high-quality code and documents. It includes a customizable workflow that makes it easy to fit seamlessly into pre-existing work processes.

  • Supports various version control systems. A few of them are Git, TFS, and CVS. 
  • Integrates with well-known project management tools and IDEs. It includes Jira, Visual Studio, and Eclipse. 
  • Analyzes team’s review process with defect metrics, custom fields, and reports.
  • Allows you to collaborate with the team on software artifacts and documents.

Review Board

A standalone code review tool that allows developers to review, discuss and track pull requests in one place. Review Board is an open-source tool that lets you conduct document reviews and can be hosted on the server.

  • Can be installed on local machines or subscribed to the cloud-based version. 
  • Supports various well-known repositories. Such as Git, Azure DevOps, and Subversion. 
  • Let you perform both pre-commit and post-commit code reviews based on your requirements
  • Allows you to automate code reviews with the help of a review bot. 
Screenshot of the Review Board dashboard


A behavioral code analysis AI tool that uses machine learning algorithms to help find code issues in the early stages and fix them before they cause obstacles. It also helps developers in managing technical debt, sound architectural decisions and improve efficiency.

  • Available in two forms: A cloud-based solution and an on-premise solution.
  • Work with any Git hosting. 
  • Support for building pipelines including Jenkins. 
  • Offers a goal-oriented workflow for planning improvements. 


A lightweight code review software by Atlassian that enables the review of codes, sharing of knowledge, discussing changes, and detecting bugs across different version control systems. It allows developers to create pre-commit reviews from IntelliJ IDEA by using the Atlassian IDE Connector.

  • Seamlessly integrate with other Atlassian products. It includes Confluence, Jira, and Bitbucket. 
  • Supported by various version control systems. Such as SVN, Git, and Mercurial. 
  • Shows how much time the reviewers took reviewing your codes.
  • Presents real-time personalized notifications and review reminders.


An open-source web-based code review tool by Google for projects with large repositories. It has Git-enabled SSH and HTTP servers that are compatible with all Git clients.

  • Acts like a Git Server and can be deployed on a public or private cloud.
  • It allows you to track bugs and review code in one place. 
  • Manages workflows with deeply integrated and delegatable access controls. 
  • Include a simple voting system to approve or reject code changes.

How do Typo help in reviewing the quality of code?

Without sounding boastful, our motivation for creating Typo was to enhance our code review process. With Typo, you have the ability to monitor crucial code review metrics, such as review duration and comprehensiveness. Additionally, it allows you to configure notifications that alert you when a code change is merged without a review or if a review has been unintentionally overlooked. There are three major metrics it tracks -

  1. Average commits after PR raised - Average number of commits after the PRs are raised
  2. PRs merged without review - Total number of PRs merged without review
  3. Bugs rate - Average number of issues raised against a story point/tasks done


Enhancing development processes goes beyond just increasing speed and quality; it brings predictability to your throughput. By leveraging Typo, you can achieve better performance and planning, ensuring consistent alignment throughout your organization.


What is a Pull Request, and Why is it Important?

Working collaboratively on a project means multiple people have different ideas and opinions. While working on an open source code with multiple people, imagine what happens if everyone starts updating the code haphazardly whenever they want to; that would be chaotic for the result.

This is where pull requests can help your team.

What is a Pull Request?

A pull request, also called a merge request, is a fundamental feature in version control systems like Git that enables developers to suggest changes to a codebase, repository, or software development project. The pull request button serves as a distinct platform for discussing and reviewing code changes and discussing the new feature. It enables keeping updates separate from the main project, promoting internal collaboration and potentially external involvement, and streamlining the debugging process.

Seriously tho : r/ProgrammerHumor

Why is a Pull Request Necessary?

A merge pull request helps developers work collaboratively. Here are five reasons it is necessary.

They Bring Efficiency into the Process

Pull requests allow developers to suggest changes and share them with the rest of the team. At the same time, it also helps them grow by getting feedback and suggestions about the fork or branch. They make space for efficient code reviews and then add the changes to the codebase in a controlled manner.

Enables Collaboration

Pull requests are a great way to encourage valuable communication and feedback between reviewers and contributors. With this platform, reviewers can leave comments directly on specific lines of code, allowing space to address concerns, ask questions, and make suggestions for improvements. This collaborative approach promotes peer review, and knowledge sharing and helps team members to develop a shared understanding, resulting in superior solutions. It also helps handle conflict resolution well within a team.

Tracks the Build Process

Pull requests play a crucial role in helping the engineering manager track the entire software build process. They serve as a central hub where developers propose changes, enabling the manager to review, provide feedback, and monitor progress. Through pull requests, the manager gains visibility into code modifications, discussions, and collaboration among team members. This allows for effective code review, quality control, and ensuring alignment with project objectives. Furthermore, pull requests often integrate with project management and continuous integration systems, providing a comprehensive view of the software build process and facilitating streamlined coordination and oversight by the engineering manager.

Code Quality Assurance

Pull requests play a vital role in ensuring code quality by acting as a gatekeeper. It facilitates a structured and collaborative process for code review, automated testing, and adhering to coding standards. Hence, ensuring that the proposed changes are aligned with the project standards, maintain code quality, and adhere to best practices.

Draft Pull Request

Draft pull request offers a critical mechanism for incremental development. Developers can work on code changes incrementally before finalizing them for integration into the main codebase. It allows for continuous feedback and developers can request review from their peers before the code is said to be complete. Hence, enhancing the software development process' flexibility and the code aligns with the project goals and standards.

Challenges of Managing Pull Request

Managing pull requests is one of the most challenging and time-consuming parts of the software development process. A few of them include:

Branching Complexity

Managing branching for each pull request may become complicated when larger projects with multiple features or bug fixes are developed concurrently. It may also happen that change in one branch leads to change in another. Therefore, the interdependency can lead to a complex branching structure.


The engineering team must ensure that the branches are properly named, isolated, and updated with the latest changes from the main codebase.

A High Number of PR

Managing a large number of pull requests is time-consuming. Especially, when the pull requests are many and very few developers to review them. This further increases the frequency of merges into the main branch which can disrupt the development workflow.


The engineering team must set a daily limit on how many PRs they can open in a day. Besides this, automated testing, continuous integration, and code formatting tools can also help streamline the process and make it easier for developers.

Merge Conflicts

During peer review, merge conflicts are a common challenge among developers. It may happen that the two developers have made changes to the same line of code. This further results in conflict as the version controller isn't sure which one to keep and which one to discard.


One of the best ways to improve team communication and using project management tools to keep track of the changes. Define areas of the codebase clearly and assign code ownership to specific team members.

Components of a Pull Request

When making a pull request, ensure you make it as easy as possible for the reviewer to approve or provide feedback. To do this well, here are the components of a good pull request:

  • Summary of changes made
  • Description of why the changes were made
  • List of files changed
  • A list of changes that were made in the pull request 
  • If applicable, include what kind of environments this should be tested on 
  • Link the web pages where this issue could possibly be used to make a change 
  • Test the proposed changes well in multiple scenarios and create test scripts for the reviewer 
  • Any relevant screenshots or other media
  • Reference to any related issues or pull requests

pull request Memes & GIFs - Imgflip

Process of Creating a Pull Request

Here are the steps to create a pull request:

Step 1: The developer creates a branch or a fork of the main project repository 

Step 2: The developer then makes the changes to this cloned code to create new features or fix an issue or make a codebase more efficient 

Step 3: This branch is pushed to the remote repository, and then a pull request is made 

Step 4: The reviewer is notified of the new changes and then provides feedback or approves the change 

Step 5: Once the change is approved, it is merged into the project repository

Process of Reviewing a Pull Request

Once a pull request is made, fellow developers can review the alterations and offer their thoughts. Their feedback can be given through comments on the pull request, proposing modifications, or giving the green light to the changes as they are. The purpose of the review stage is to guarantee that the changes are of top-notch quality, adhere to the project's criteria, and align with the project's objectives.

If there are any changes required to be made, the developer is alerted for updating process. If not, a merging process takes place where the changes are added to the codebase.

Best Practices for Using Pull Requests

Some best practices for using pull requests include:

  • Creating small, focused pull requests that address one issue or feature at a time
  • Providing clear explanations and context for the changes made
  • Responding promptly to feedback and comments
  • Ensuring that all automated tests pass before creating a pull request
  • Using a code review checklist to ensure that changes meet project standards and guidelines

Why PR is Important for Code Reviews?

The code review process significantly contributes to extended cycle times, particularly in terms of pull request pickup time, pull request review time, and pull request size. Understanding the importance of measurement for improvement, we have developed a platform that aggregates your issues, Git, and release data into one centralized location. However, we firmly believe that metrics alone are not sufficient for enhancing development teams.

While it is valuable to know your cycle time and break it down into coding time, PR pickup time, PR review time, and deploy time, it is equally important to assess whether your average times are considered favorable or unfavorable.

Screenshot 2024-03-16 at 1.07.29 AM.png

At Typo, we strive to provide not only the data and metrics but also the context and insights needed to gauge the effectiveness of your team’s performance. By combining quantitative metrics with qualitative analysis, our platform empowers you to make informed decisions and drive meaningful improvements in your development processes.

We understand that achieving optimal performance requires a holistic approach, and we are committed to supporting your team’s success.

Top 10 DevOps Influencers (2024)

DevOps has been quickly making its way into every prime industry. Especially in a software development field where it is necessary to integrate DevOps in today’s times.

To help you with the latest trends and enhance your knowledge on this extensive subject, we have hand-picked the top 10 DevOps influencers you must follow. Have a look below:

Best DevOps influencers to follow

James Turnbull: 

James is best known for his contribution to the open-source software industry. He also posts prolifically about DevOps-related topics including software issues, network monitoring tools, and change management. 

James has also been the author of 10 books. A few of them are The Docker Book, The Art of Monitoring, and Monitoring with Prometheus. He regularly speaks at well-known conferences such as FOSDEM, OSCON, and 


Nicole is an influential voice when it comes to the DevOps community. She is a Co-founder of DevOps Research and Assessment LLC (now part of Google). As a research and strategy expert, Nicole also discusses how DevOps and tech can drive value to the leaders. 

Besides this, she is a co-author of the book Accelerate: The Science of Lean Software and DevOps. Nicole is also among the Top 10 thought leaders in  DevOps and the Top 20 most influential women in DevOps. 


Founder of Devopsdays, Patrick has been a researcher and consultant with several companies in the past. He focuses on the development aspect of DevOps and analyzes past and current trends in this industry. He also communicates insights on potential future trends and practices. 

But this is not all! Patrick also covers topics related to open-source technologies and tools, especially around serverless computing.  


A frequent speaker and program committee member for tech conferences. Bridget leads Devopsdays - A worldwide conference service. She also has a podcast ‘Arrested DevOps’ where she talks about developing good practices and maximizing the potential of the DevOps framework. 

Bridget also discusses Kubernetes, cloud computing, and other operations-related topics. 


Best known for the newsletter 'DevOps Weekly’, Gareth covers the latest trends in the DevOps space. A few of them include coding, platform as a service (PaaS), monitoring tools for servers and networks, and DevOps culture. 

Gareth also shares his valuable experience, suggestions, and thoughts with the freshers and experienced developers, and leaders. 


Elisabeth Hendrickson is the founder and CTO of Curious duck digital laboratory. She has been deeply involved in software development and the DevOps community for more than a decade. She has authored books on software testing and teamwork within the industry. It includes Explore it and Change your Organization

Elisabeth has also been a frequent speaker at testing, agile, and DevOps conferences.

Elisabeth Hendrickson

Martin is the author of seven books based on software development. It ranges from design principles, people, and processes to technology trends and tools. A few of them are: Refactoring: Improving the Design of Existing Code and Patterns of Enterprise Application Architecture

He is also a columnist for various software publications. He also has a website where he talks about emerging trends in the software industry. 


Known as the prolific voice in the DevOps community, John has been involved in this field for more than 35 years. He covers topics related to software technology and its impact on DevOps adoption among organizations. 

John has co-authored books like The DevOps Handbook and Beyond the Phoenix Project. Besides this, he has presented various original presentations at major conferences. 


Gene is a globally recognized DevOps enthusiast and a best-seller author within the IT industry. He focuses on challenges faced by DevOps organizations and writes case studies describing real-world experiences. 

His well-known books include The Unicorn Project, The DevOps Handbook, and The Visible Ops Handbook. Gene is also a co-founder of Tripwire - A software company. He has been a keynote speaker at various conferences too. 


Jez is an award-winning author and software researcher. A few of his books are The DevOps Handbook, Accelerate: The Science of Lean Software and DevOps, and Lean Enterprise

Jez focuses on software development practices, lean enterprise, and development transformation. He is also a popular speaker at the biggest agile and DevOps conferences globally. 



It is important to stay updated with DevOps influencers and other valuable resources to get information on the latest trends and best practices.

Make sure you follow them (or whom you find right) to learn more about this extensive field. You’ll surely get first-hand knowledge and valuable insights about the industry.


6 Effective Ways to Reduce Technical Debt

Technical debt is a common concept in software development. Also known as Tech debt or Code debt, It can make or break software updates. If this problem is not solved for a long time, its negative consequences will be easily noticeable.

In this article, let’s dive deeper into technical debt, its causes, and ways to address them.

What is Technical Debt?

‘Technical Debt’ was coined by Ward Cunningham in 1992. It arises when software engineering teams take shortcuts to develop projects. This is often for short term gains. In turn, this leads to creating more work for themselves. Since they choose the quickest solution rather than the most effective solution.

It could be because of insufficient information about users’ needs, pressure to prioritize release over quality or not paying enough attention to code quality.

However, this isn’t always an issue. But, it can become one when a software product isn’t optimized properly or has excessively dysfunctional code.

When Technical debt increases, it can cause a chain reaction that can also spill into other departments. It can also result in existing problems getting worse over time.

Examples of Technical Debt

Below are a few technical debt examples:

  • A poor-quality software code is an evident example of technical debt. This could be due to many reasons including not adhering to coding standards, and lack of training for new developers.
  • Due to insufficient software testing, technical debt becomes common in those organizations. The reasons behind this could be a lack of quality assurance support or not implemented DevOps yet.
  • Copying and pasting code snippets might be a quick fix. However, it results in maintenance challenges and the need for updates in multiple places.
  • When periodic refactoring is neglected, it leads to the accumulation of technical debt over time.

Causes of Technical Debt

Business Causes

Prioritizing business needs and the company’s evolving conditions can put pressure on development teams to cut corners. It can result in preponing deadlines or reducing costs to achieve desired goals; often at the expense of long-term technical debt cost. Insufficient technological leadership and last-minute changes can also lead to misalignment in strategies and funding.

Development Causes

As new technologies are evolving rapidly, It makes it difficult for teams to switch or upgrade them quickly. Especially when already dealing with the burden of bad code.

Unclear project requirement is another cause of technical debt. As it leads to going back to the code and reworking it. Lack of code documentation or testing procedures is another reason for technical debt.

Human Resources Causes

When team members lack the necessary skills or knowledge to implement best practices, unintentional technical debt can occur. It can result in more errors and insufficient solutions.

It can also be due to when the workload is distributed incorrectly or overburdened which doesn’t allow teams to implement complex and effective solutions.

Frequent turnovers or a high attrition rate is another factor. As there might be no proper documentation or knowledge transfer when one leaves.

Resources Causes

As mentioned above, time and resources are major causes of technical debt. When teams don’t have either of them, they take short cuts by choosing the quickest solution. It can be due to budgetary constraints, insufficient processes and culture, deadlines, and so on.

How Technical Debt can Impact Business?

Managing technical debt is a crucial step. If not taken care of, it can hinder an organization's ability to innovate, adapt, and deliver value to its customers.

Lost Opportunities

Just like how financial debt can narrow down an organization's ability to invest in new projects, technical debt restricts them from pursuing new projects or bringing new features. Hence, resulting in missed revenue streams.

Results in Design Debt

When the development team fixes immediate issues caused by technical debt; it avoids the root cause which can accumulate over time and result in design debt - a suboptimal system design.

Leads to Customer Churn

When tech debt prevails in the long run, it can result in the new features being delayed or a miss in delivery deadlines. As a result, customers can become frustrated and seek alternatives.

Ways to Reduce Technical Debt

The vicious cycle of technical debt begins with short cuts and compromises accumulate over time. Below are a few ways to reduce 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. It also helps uncover issues that are not easily detected through manual testing.

Automated testing also serves as a backbone for other practices that improve code quality such as code refactoring.

Regular Code Reviews

Code review in routine allows 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.

Code reviews also give valuable input on code structure, scalability, and modularity. It allows engineers to look at the bugs or design flaws in the development issues. There needs to be a document stating preferred coding practices and other important points.


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.

Refactoring sped things up and improves clarity, readability, maintainability, and performance.

But, as per engineering teams, it could be risky and time-consuming. Hence, it is advisable to get everyone on the same page. Acknowledge technical debt and understand why refactoring can be the right way.

Track with the Right Metrics

Engineering metrics are a necessity. It helps in tracking the technical debt and understanding what can be done instead. A few of the suggestions are:

  • Defect Escape Rate
  • Cycle Time
  • Lead Time
  • Deployments Per Day
  • Cumulative Flow

Identify the key metrics that are suitable for measuring technical debt in the software development process. Ensure that the teams have SMART goals that are based on organizational objectives. Accordingly, focus on the identified issues and create an actionable plan.

Adhere to Agile Methodology

Agile Development Methodology, such as Scrum or Kanban, promotes continuous improvement and iterative development, aligning seamlessly with the principles of the Agile manifesto.

It breaks down the development process into smaller parts or sprints. As Agile methodology emphasizes regular retrospectives, it helps in reflecting on work, identifying areas for improvement, and discussing ways to address technical debt.

By combining agile practices with a proactive approach, teams can effectively manage and reduce it.

Listen to your Engineers

Last but not the least! Always listen to your engineers. They are the ones who are well aware of ongoing development. They are working closely with a database and developing the applications. Listen to what they have to say and take their suggestions and opinions. It helps in gaining a better understanding of the product and getting valuable insights.

Besides this, when they know they are valued at the workplace, they tend to take ownership to address technical debt.

Addressing Technical Debt with Typo

To remediate technical debt, focus on resources, teams, and business goals. Each of them is an important factor and needs to be taken into consideration.

With Typo, enable your development team to code better, deploy faster, and align with the business goals. With the valuable insights, gain real-time visibility into SDLC metrics and identify bottlenecks. Not to forget, keep a tap on your teams’ burnout level and blind spots they need to work on.


To remediate technical debt, focus on resources, teams, and business goals. Since each of them is important factors and needs to be taken into consideration.


Top 10 Newsletters for CTOs and Engineering Leaders

Software engineering is an evolving industry. You need to be updated on the latest trends, best practices, and insights to stay ahead of the curve.

But, engineering managers and CTOs already have a lot on their plate. Hence, finding it difficult to keep up with the new updates and best practices.  

This is when engineering newsletters come to the rescue!

They provide you with industry insights, case studies, best practices, tech news, and much more.

Check out the top 10 newsletters below worth subscribing to:

Best newsletters for Engineering Managers and CTOs

Software Lead Weekly:

It is defined as the ‘Best curated and most consistently excellent list’ by tech leads. Software Lead Weekly is curated for tech leads and managers to make them more productive and learn new skills. It contains interviews with experts, CTO tips, industry insights, in-depth software development process, and tech market overview to name a few.

  • Delivery frequency: Weekly 
  • Author: Oren Ellenbogen, Vice president at Forter and author of 'The Leadership Role @ The Agile Era' and 'Leading Snowflakes'. 

Top 10 newsletters for CTOs and engineering leaders

Level Up:

This is a weekly newsletter geared towards the tech leads, engineering managers, and CTOs. The author, Patrick Kua shares his reflection and experiences of software engineering, current tech trends, and industry changes. The newsletter also dives deep into trends around tech, leadership, architecture, and management.

  • Delivery frequency: Weekly

Top 10 newsletters for CTOs and engineering leaders


The refactoring delivers an essay-style newsletter for managers, founders, and engineers. It sheds light on becoming better leaders and building engineering teams. The author, Luca Rossi also talks about the experiences and learnings in the engineering industry. With the illustrations and explanatory screenshots, the newsletter can also be read by newbie engineers.

  • Delivery frequency: Weekly

Top 10 newsletters for CTOs and engineering leaders

Effective Software Leads

This monthly newsletter covers the challenges of building and leading software teams in the 21st century. It includes interesting engineering articles, use cases, and insights from engineering experts. It also provides a solution to the common software engineering problems the CTOs and managers face.

  • Delivery frequency: Monthly

Top 10 newsletters for CTOs and engineering leaders

The Pragmatic Engineer

It is known as the Number 1 technology newsletter on substack.’ This newsletter is a valuable resource for team leaders and senior engineers. Each edition contains CTO tips and best practices, trending topics, and engineering-related stories. It also deep dives into engineering culture, the hiring and onboarding process, and related careers.

  • Delivery frequency: Two times a week
  • Author: Gergely Orosz, Software and Engineering manager (Ex-uber, Ex-skype/Microsoft)

The Pragmatic Engineer

Tech Manager Weekly

Tech Manager Weekly is informative and helpful for tech managers. Their editions are short and informative and provide insights into various engineering topics. Software development process, tech news, tech trends, industry insights, and CTOs tips to name a few. The newsletter - Tech Manager Weekly also provides information on how various companies use technologies.

  • Delivery frequency: Weekly
  • Author: CTO Craft – A learning and coaching network built for engineering managers, CTOs, and tech leads.

Tech Manager Weekly


This newsletter is written in an easy-to-understand and crisp format. In each edition, it delivers the latest technology and software news around the world. The newsletter also covers important science and coding stories as well as futuristic technologies.

  • Delivery frequency: Daily


Leadership Garden

This newsletter focuses majorly on developers’ productivity. It covers topics such as giving actionable guidance to leaders and how they can create people-first culture. The newsletter also includes what’s happening around the other tech companies in terms of work culture and productivity.

Leadership Garden

The Sequence

These bite-sized newsletters keep you abreast of the situation in AI, machine learning, and data science. It also includes the most important research paper, tech release, and VC funding. You can also find interviews with researchers, and engineers, in the machine learning field.

  • Delivery frequency: Weekly
  • Author: Jesus Rodriguez, Co-founder and CEO of Intotheblock, and Ksenia Se, Founder at Turing Post and Co-founder and Editor at TheSequence



Bytebytego is considered to be one of the best tech newsletters worth reading for engineering managers and CTOs. It converts complex systems into simple terms and deep dives into one design per edition. The newsletter also covers trending topics related to large-scale system design.

  • Delivery frequency: Weekly
  • Author: Alex Xu, Founder at Bytebytego and author of ‘System Design Interview Series’


Benefits of Subscribing to the Newsletter

CTOs and engineering leaders should subscribe to newsletters for the various compelling reasons:

Provides the Latest News and Industry IT Trends

These newsletters are beneficial as they deliver the latest IT news, industry trends, technological advancements, and CTO best practices right to your inbox.

Provides Networking Opportunities

These newsletters may also include information regarding events, workshops, conferences, and other network opportunities for CTOs and tech leaders.

Thought Leadership

Through these newsletters, CTOs and engineering leaders can get exposure to thought and tech leadership content from experts in technology and management.


Keeping up with a wide variety of engineering topics could be a bit tricky. Newsletters make it easier to stay on top of what's going on in the tech world.

The newsletters we mentioned above are definitely worth reading. Pick the ones that meet your current requirements - and subscribe!


10 best engineering blogs you must read

There are various sources of information from which engineers can gain knowledge. But, one of the valuable resources on which even the senior engineers rely is the blogs. These engineering blogs are written by experts who share various aspects of engineering.

These blogs cover a wide range of engineering topics. Such as big data, machine learning, engineering business and ethics, and so on.

Here are 10 blogs that every engineer must read to help them broaden their knowledge base:

Engineering blogs You Must Read

Netflix Engineering Blog

Netflix is a well-known streaming service that offers a wide range of movies, series, documentaries, anime, Kdrama, and much more. They also have a tech blog where their engineers share their learnings. They also discuss topics such as machine learning, strong engineering culture, and databases. In short, they cover everything from the beginning until today’s Netflix era.

Recommended Blogs:

Pinterest Engineering Blog

Pinterest is an image-focused platform where users can share and discover new interests. Their tech blog includes content on various engineering topics. Such as data science, machine learning, and technologies to keep their platform running. It also discusses coding and engineering insights and ideas.

Recommended Blogs:

Slack Engineering Blog

Slack is a collaboration and communication hub for businesses and communities. They have an engineering blog where its experts discuss technical issues and challenges. They also publish use cases and current topics from the software development world.

Recommended Blogs:

Quora Engineering Blog

Quora is a platform where users can ask and answer questions. Their tech blog majorly discusses the issues they face on both the front and backend side. Quora also talk about how they build their platform. It also covers a wide range of engineering topics. Some of them are natural languages model, machine learning, and NLP.

Recommended Blogs:

Heroku Engineering Blog

Heroku is a cloud platform where developers deploy, manage and scale modern applications. It runs a tech blog where they discuss deployment issues and various software topics. They also provide code snippets, and tutorials to improve the developer’s skills.

Recommended Blogs:

Spotify Engineering Blog

Spotify is the largest audio streaming platform which includes songs and podcasts. In their engineering blogs, they talk about the math behind their platform’s advanced algorithm. Spotify also provides insights on various engineering topics. This includes infrastructure, databases, open source, software development life cycles, and much more.

Recommended Blogs:

GitHub Engineering Blog

GitHub is a well-known hosting site for collaboration and version control. They cover workflow topics and related issues in their blog. It also helps developers understand the platform better by discussing their new features, innovations, and DevOps.

Recommended Blogs:

Meta Engineering Blog

Meta is a parent company of Facebook. It also owns other popular social media platforms – Instagram and Whatsapp. Its engineering blog covers a wide variety of topics such as Artificial intelligence, machine learning, and infrastructure. Meta also discusses how it solves large-scale technical challenges and current engineering topics.

Recommended Blogs:

Linkedin Engineering Blog

Linkedin is the largest social platform where professionals can network, share and learn. In their engineering blog, they share their learnings and challenges while building their platform. Linkedin also provides insights into various software and applications they have used.

Recommended Blogs:

Reddit Engineering Blog

Reddit is a popular news and discussion platform where users create and share content. They have a subreddit where they cover a variety of topics such as tech and engineering issues. Besides this, Reddit’s engineers open up about the challenges and perspectives they face in their fields.

Recommended Blogs:

Typo Engineering Blog

Typo is a well-known engineering management blog. They provide valuable insights on various engineering-related topics. It includes DORA metrics, developer productivity, and code review to name a few. Typo also covers leading tools, newsletters, and blogs to help developers keep up with the trends and skills.

Recommended Blogs:


We have curated a few of the best blogs engineers can follow. Hope these blogs help engineers to gain a deeper understanding and insights.

Happy learning! :) 


Best Tools for Improving SDLC in 2024

SDLC is an iterative process from planning to deployment and everything in between. When applied, it can help in producing high-quality, sustainable low-cost software in the shortest time possible.

But, the process isn’t as simple as it sounds. There are always bug fixes and new features to improvise your product. Hence, you need the right tools to make it simple and quick.

Best tools for Improving SDLC

Engineering Management Tools


Typo is an intelligent engineering management platform. It is used for gaining visibility, removing blockers, and maximizing developer effectiveness. Through SDLC metrics, you can ensure alignment with business goals and prevent developer burnout. This tool can be integrated with the tech stack to deliver real-time insights. Git, Slack, Calenders, and CI/CD to name a few.

Typo Key Features:

  • Cycle time breakdown
  • Work log
  • Investment distribution
  • Goal setting for continuous improvement
  • Developer burnout alert
  • PR insights
  • Developer workflow automation

Screenshot 2024-03-16 at 12.29.43 AM.png



GitHub is a popular git repository hosting service for code sharing. It is a cloud-based tool that allows you to configure, control and maintain code bases with your team. It also offers features such as bug tracking, feature request, and task management. Github’s supported platforms include Windows, Linux, MacOS, Android, and IOS.

GitHub Key Features:

  • Easy project management
  • Easy code hosting
  • Track and assign tasks
  • Navigate files through the repository easily
  • Host simple websites for free


Bitbucket is the largest version repository hosting service owned by Atlassian. It provides unlimited private code repositories for Git. Besides this, it also offers issue tracking, continuous delivery, and wikis. The supported platforms for Bitbucket include Linux, AWS, Microsoft, and Azure.

Bitbucket key Features:

  • Secure platform for code with SOC 2 Type 2 certification
  • Pull requests and code reviews
  • Support GIT large file storage (LFT)
  • External authentication support for GitHub, Facebook, Google, and Twitter
  • Offers advanced search for codes

Issue Tracker


Jira is an issue-tracking product that tracks defects and manages bugs and agile projects. It has three main concepts: Project, issue, and workflow. Available on Windows, Linux, Amazon Web Services, and Microsoft Azure, Jira can be integrated with various engineering tools. A few of them include Zephyr, GitHub, and Zendesk.

Jira Key Features:

  • Create and share complex roadmaps
  • Relevant information available in form of reports
  • Real-time notifications
  • Time tracking with color integration
  • Ready to use dashboards and reports


Linear is an issue-tracking tool for high-performing teams. It is used for streamlining software projects, tasks, and bug tracking. The majority of repeated work is automated already which makes the SDLC activities faster. It has more than 2200 integrations available such as Slack, Gitlab, and The supported platforms for linear are MacOS intel, MacOS silicon, and Windows.

Linear Key Features:

  • Lightweight and fast
  • Real-time sync
  • Works in offline mode as well
  • Can create multiple teams and reside in a shared workspace
  • Highly detailed issue tracking

Linear – A better way to build products


ClickUp is a leading issue-tracking and productivity tool. It is highly customizable that lets you streamline issue-tracking and bug-reporting processes. It has powerful integrations with applications such as Gitlab, Figma, and Google Drive. ClickUp is available on Windows and Android.

ClickUp Key Features:
  • Customizable bug-tracking views
  • ClickUp docs for creating roadmaps and outline process
  • Offers a wide range of templates
  • Supporting programming languages including HTML, Python, CMS, PHP, and many more
  • Sync with Google Calendar

Communication Platforms


Slack is a popular communication tool for engineering leaders and developers. It provides real-time visibility into project discussions and growth. This tool is available for many platforms such as Web, Windows, MacOS, Android, IOS, and Linux. Slack has an extensive app directory that lets you integrate engineering software and custom apps.

Slack Key Features:

  • Simple and intuitive interface
  • Video and voice calls via Huddles
  • Can create public and private channels
  • Get notifications from Jira, Jenkins, and many more in slack channels directly
  • Remove unnecessary friction by bringing engineering leaders and developers to one place

Slack Notifications: Set Up, Manage, And Create Your Strategy Here

Microsoft Teams

Microsoft Teams streamlines communication and collaboration in a single platform. It assists in keeping up to date with development, testing, and deployment activities. Available for Web, IOS, Android, Windows, and MacOS, MS teams include built-in apps and integrations.

Microsoft Teams Key Features:

  • Ship software remotely within the cloud-hosted environment
  • Quick polls option
  • Engineering project templates available
  • Easy and structured collaboration
  • Build 3D objects in an in-built PowerPoint application


Discord facilitates real-time discussions and communication. It is available on various platforms which include Windows, MacOS, Linux, Android, and IOS. It has an advanced video and voice call feature to collaborate for SDLC activities.

Discord Key Features:

  • Video and voice chat functionality comes with advanced features
  • Simple and easy to use
  • Direct messages and channels
  • Channels accessible according to the hierarchy

Continuous Integration and Continuous Delivery Tools (CI/CD)


Jenkins is one of the popular CI/CD tools for developers. It is a Java-based tool that produces results in minutes and provides real-time testing and reporting. Jenkins is available for MacOS, Windows, and Linux platforms. It also offers an extensive plug-ins library to integrate with other development tools. Github, Gitlab, and Pipeline to name a few.

Jenkins Key Features:

  • As it is Java-based, it can run on all platforms
  • A completely free and open-source tool
  • Easy and user-friendly interface
  • Build schedules based on expressions
  • Strong support from large online communities of the Agile team

Azure DevOps

Azure DevOps by Microsoft is a comprehensive CI/CD platform. It ensures that the entire software development delivery is done in a single place. From automating, building, and testing code, Azure DevOps brings together developers, product managers, and other team members. This tool has cloud-hosted pipelines available for MacOS, Windows, and Linux. Besides this, it has an integration of over 1000 apps built by the Azure community.

Azure DevOps Key Features:

  • A self-hosted CI/CD model
  • Clean user interface
  • Secure at all times through encryption techniques including SSL/TLS or IPSec VPN tunnels
  • Supports manual and exploratory testing
  • Adaptability to market demands

AWS Codepipeline

AWS Codepipeline is an ideal CI/CD tool for AWS users. It helps in automating your build, release, and pipeline CI/CD processes. AWS Codepipeline also offers fast and reliable application and infrastructure updates. With easy steps, you can set up Codepipeline in your AWS account in a few minutes. This tool can also be integrated with third-party servers. It includes GitHub or your custom plugin.

AWS Codepipeline Key Features:

  • Supports code building, testing, and compilation
  • Rapid delivery of new features and updates
  • Pre-built and custom plug-ins
  • Receive notifications for events
  • Increase developer output

Code Quality Tools


SonarQube is a popular static code analysis tool. It is used for continuous code inspection of code security and quality. The quality gate in this tool blocks any code that doesn’t reach a certain quality. It stops the code from going into production. It integrates with various code repositories such as GitHub, Bitbucket, and GitLab. SonarQube’s supported platforms are MacOS, Windows, and Linux.

SonarQube Key Features:

  • Supports on-premise and cloud setup
  • Supporting programming languages including Python, C#, and Java
  • Generates detailed code reports
  • Identify tricky issues
  • Identify security vulnerabilities in the code and emphasize them is a code analysis and review tool that helps you to get an overview of the code base. It also allows you to get a glance at a whole project, recent commits, and problematic files. The powerful integrations of are GitHub and Bitbucket. Key Features:

  • Easy setup
  • Offers analytical data
  • Create comments directly from the code
  • Supports programming languages such as HTML, Java, PHP, and Python
  • Provide real-time actionable feedback for potential quality issues

Test Automation Tools


Selenium is a powerful tool for web-testing automation. It is implemented by organizations of different industries to support an array of initiatives including DevOps, Agile model, and Continuous delivery. Selenium is one of the best test automation tools that can be automated across various Os. It includes Windows, Mac, and Linux as well as browsers such as Chrome, Firefox, IE, Microsoft Edge, Opera, and Safari.

Selenium Key Features:

  • Executes test in parallel with Selenium grid
  • Supports programming languages such as Ruby, Java, Perl, and C#
  • Includes various customization options
  • Testing on local or remote machines via the Selenium server
  • Supports mobile web applications on various platforms like Android, iPhone, and Blackberry


LambdaTest is one of the well-known test automation tools that provides cross-platform compatibility. It can be used with simulated devices on the cloud or locally deployed emulators. This tool can be integrated with a variety of frameworks and software tools. It includes Selenium, Cypress, Playwright, Puppeteer, Taiko, Appium, Espresso and XCUITest.

LambdaTest Key Features:

  • A cloud-based cross-browser testing platform
  • Automated reports downloadable in different formats
  • Testing web apps in multiple operating systems, including Windows and Mac
  • Geolocation web testing across over 27 countries
  • Create and run both manual and automatic tests


Cypress is an open source automation tool for front-end developers that operates with a programming language – JavaScript framework. It is one of the popular test automation tools that focuses on end-to-end testing. It is built upon a new architecture, hence, it can directly operate within a browser in the same run-loop as your application.

Cypress Key Features:

  • Installation of this tool is simple
  • Test runs in any modern browser
  • Can easily get the test snapshots from the command log
  • Supported programming language is JavaScript
  • Connects to Cypress Cloud for test performance and optimization

Code Review Tools


It is one of the automated code review tools for static analysis. Supporting more than 40+ programming languages, Codacy also integrates with various popular tools and CI/CD workflows.

Codacy Key Features:

  • Gives a clear picture of how projects are doing and how their quality is changing over time
  • Adaptable to your code review process
  • Provides code coverage
  • Helps in knowing the amount of technical debt
  • Can identify duplication and poor code metric areas


One of the code review tools that is built on a SaaS model. It helps in analyzing code from a security standpoint.

Veracode Key Features:

  • Gives test coverage by using binary code/bytecode
  • Automates different workflows with integrated and streamlined testing in SDLC
  • Offers set of two code review tools – Static analysis and software composition analysis
  • Provides mitigation management
  • Integrated into existing debugging systems

AI Pair Programmer

GitHub Co-Pilot

GitHub Co-pilot is an AI pair programmer that uses open AI codex for writing code quickly. The programmer is trained in natural language and publicly available source code that makes it suitable for programming and human languages. The aim is to speed up the development process and increase developers’ productivity. It draws context from the code and suggests whole lines or complete functions. GitHub works the most efficiently with few programming languages. These include Typescript, Javascript, Ruby, Python, GO, C#, and C++. It can be integrated with popular editors. It includes Neovim, JetBrains IDEs, Visual Studio, and Visual Studio Code. However, you need to install visual studio code for using GitHub on this platform.

GitHub Co-pilot Key Features:

  • Create predictive lines of code from comments and existing patterns in the code
  • Suitable for programmers and human languages
  • The more context you offer, the more accurate and usable it is
  • Easy to use
  • Good at writing boilerplate code
  • Improved code navigation
  • Create dictionaries of lookup data

GitHub Copilot · Your AI pair programmer · GitHub


These tools can assist you well while you work on SDLC activities.

In this article, we have highlighted some of the well-known tools for your team. You can research more about them to know what fits best for your team.

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