How does Gen AI address Technical Debt?

The software development field is constantly evolving field. While this helps deliver the products and services quickly to the end-users, it also implies that developers might take shortcuts to deliver them on time. This not only reduces the quality of the software but also leads to increased technical debt.

But, with new trends and technologies, comes generative AI. It seems to be a promising solution in the software development industry which can ultimately, lead to high-quality code and decreased technical debt.

Let’s explore more about how generative AI can help manage technical debt!

Technical debt: An overview

Technical debt arises when development teams take shortcuts to develop projects. While this gives them short-term gains, it increases their workload in the long run.

In other words, developers prioritize quick solutions over effective solutions. The four main causes behind technical debt are:

  • 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.
  • 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.
  • Human resources causes: Unintentional technical debt can occur when development teams lack the necessary skills or knowledge to implement best practices. It can result in more errors and insufficient solutions.
  • Resources causes: When teams don’t have time or sufficient resources, they take shortcuts by choosing the quickest solution. It can be due to budgetary constraints, insufficient processes and culture, deadlines, and so on.

Why generative AI for code management is important?

As per McKinsey’s study,

“… 10 to 20 percent of the technology budget dedicated to new products is diverted to resolving issues related to tech debt. More troubling still, CIOs estimated that tech debt amounts to 20 to 40 percent of the value of their entire technology estate before depreciation.”

But there’s a solution to it. Handling tech debt is possible and can have a significant impact:

“Some companies find that actively managing their tech debt frees up engineers to spend up to 50 percent more of their time on work that supports business goals. The CIO of a leading cloud provider told us, ‘By reinventing our debt management, we went from 75 percent of engineer time paying the [tech debt] ‘tax’ to 25 percent. It allowed us to be who we are today.”

There are many traditional ways to minimize technical debt which includes manual testing, refactoring, and code review. However, these manual tasks take a lot of time and effort. Due to the ever-evolving nature of the software industry, these are often overlooked and delayed.

Since generative AI tools are on the rise, they are considered to be the right way for code management which subsequently, lowers technical debt. These new tools have started reaching the market already. They are integrated into the software development environments, gather and process the data across the organization in real-time, and further, leveraged to lower tech debt.

Some of the key benefits of generative AI are:

  • Identify redundant code: Generative AI tools like Codeclone analyze code and suggest improvements. This further helps in improving code readability and maintainability and subsequently, minimizing technical debt.
  • Generates high-quality code: Automated code review tools such as Typo help in an efficient and effective code review process. They understand the context of the code and accurately fix issues which leads to high-quality code.  
  • Automate manual tasks: Tools like Github Copilot automate repetitive tasks and let the developers focus on high-quality tasks.
  • Optimal refactoring strategies: AI tools like Deepcode leverage machine learning models to understand code semantics, break it down into more manageable functions, and improve variable namings.

Case studies and real-life examples

Many industries have started adopting generative AI technologies already for tech debt management. These AI tools assist developers in improving code quality, streamlining SDLC processes, and cost savings.

Below are success stories of a few well-known organizations that have implemented these tools in their organizations:

Microsoft uses Diffblue cover for Automated Testing and Bug Detection

Microsoft is a global technology leader that implemented Diffblue cover for automated testing. Through this generative AI, Microsoft has experienced a considerable reduction in the number of bugs during the development process. It also ensures that the new features don’t compromise with existing functionality which positively impacts their code quality. This further helps in faster and more reliable releases and cost savings.

Google implements Codex for code documentation

Google is an internet search engine and technology giant that implemented OpenAI’s Codex for streamlining code documentation processes. Integrating this AI tool helped subsequently reduce the time and effort spent on manual documentation tasks. Due to the consistency across the entire codebase, It enhances code quality and allows developers to focus more on core tasks.

Facebook adopts CodeClone to identify redundancy

Facebook, a leading social media, has adopted a generative AI tool, CodeClone for identifying and eliminating redundant code across its extensive codebase. This resulted in decreased inconsistencies and a more streamlined and efficient codebase which further led to faster development cycles.

Pioneer Square Labs uses GPT-4 for higher-level planning

Pioneer Square Labs, a studio that launches technology startups, adopted GPT-4 to allow developers to focus on core tasks and let these AI tools handle mundane tasks. This further allows them to take care of high-level planning and assist in writing code. Hence, streamlining the development process.

How Typo leverage generative AI to reduce technical debt?

Typo’s automated code review tool 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.

Typo also auto-analyses your codebase pulls requests to find issues and auto-generates fixes before you merge to master. Its Auto-Fix feature leverages GPT 3.5 Pro trained on millions of open source data & exclusive anonymised private data as well to generate line-by-line code snippets where the issue is detected in the codebase.

As a result, Typo helps reduce technical debt by detecting and addressing issues early in the development process, preventing the introduction of new debt, and allowing developers to focus on high-quality tasks.

Issue detection by Typo

AI to reduce technical debt

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

AI to reduce technical debt

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.

Professional coding standards

Click here to know more about our Code Review tool

Can technical debt increase due to generative AI?

While generative AI can help reduce technical debt by analyzing code quality, removing redundant code, and automating the code review process, many engineering leaders believe technical debt can be increased too.

Bob Quillin, vFunction chief ecosystem officer stated “These new applications and capabilities will require many new MLOps processes and tools that could overwhelm any existing, already overloaded DevOps team,”

They aren’t wrong either!

Technical debt can be increased when the organizations aren’t properly documenting and training development teams in implementing generative AI the right way. When these AI tools are adopted hastily without considering any long-term implications, they can rather increase the workload of developers and increase technical debt.

Ethical guidelines

Establish ethical guidelines for the use of generative AI in organizations. Understand the potential ethical implications of using AI to generate code, such as the impact on job displacement, intellectual property rights, and biases in AI-generated output.

Diverse training data quality

Ensure the quality and diversity of training data used to train generative AI models. When training data is biased or incomplete, these AI tools can produce biased or incorrect output. Regularly review and update training data to improve the accuracy and reliability of AI-generated code.

Human oversight

Maintain human oversight throughout the generative AI process. While AI can generate code snippets and provide suggestions, the final decision should be upon the developers for final decision making, review, and validate the output to ensure correctness, security, and adherence to coding standards.

Most importantly, human intervention is a must when using these tools. After all, it’s their judgment, creativity, and domain knowledge that help to make the final decision. Generative AI is indeed helpful to reduce the manual tasks of the developers, however, they need to use it properly.


In a nutshell, generative artificial intelligence tools can help manage technical debt when used correctly. These tools help to identify redundancy in code, improve readability and maintainability, and generate high-quality code.

However, it is to be noted that these AI tools shouldn’t be used independently. These tools must work only as the developers’ assistants and they muse use them transparently and fairly.

Use of AI in the code review process

The code review process is one of the major reasons for developer burnout. This not only hinders the developer’s productivity but also negatively affects the software tasks. Unfortunately, it is a crucial aspect of software development that shouldn’t be compromised.

So, what is the alternative to manual code review? Let’s dive in further to know more about it:

The current State of Manual Code Review

Manual code reviews are crucial for the software development process. It can help identify bugs, mentor new developers, and promote a collaborative culture among team members. However, it comes with its own set of limitations.

Software development is a demanding job with lots of projects and processes. Code review when done manually, can take a lot of time and effort from developers. Especially, when reviewing an extensive codebase. It not only prevents them from working on other core tasks but also leads to fatigue and burnout, resulting in decreased productivity.

Since the reviewers have to read the source code line by line to identify issues and vulnerabilities, it can overwhelm them and they may miss out on some of the critical paths. This can result in human errors especially when the deadline is approaching. Hence, negatively impacting project efficiency and straining team resources.

In short, manual code review demands significant time, effort, and coordination from the development team.

This is when AI code review comes to the rescue. AI code review tools are becoming increasingly popular in today’s times. Let’s read more about AI code review and why is it important for developers:

What is AI Code Review?

AI code review is an automated process that examines and analyzes the code of software applications. It uses artificial intelligence and machine learning techniques to identify patterns, detect potential problems, common programming mistakes, and potential security vulnerabilities. These AI code review tools are entirely based on data so they aren’t biased and can read vast amounts of code in seconds.

Why AI in the Code Review Process is Important?

Augmenting human efforts with AI code review has various benefits:

Enhance Overall Quality

Generative AI in code review tools can detect issues like potential bugs, security vulnerabilities, code smells, bottlenecks, and more. The human code review process usually overlooks these issues. Hence, helping in identifying patterns and recommending code improvements that can enhance efficiency and maintenance and reduce technical debt. This leads to robust and reliable software that meets the highest quality standards.

Improve Productivity

AI-powered tools can scan and analyze large volumes of code within minutes. It not only detects potential issues but also suggests improvements according to coding standards and practices. This allows the development team to catch errors early in the development cycle by providing immediate feedback. This saves time spent on manual inspections and rather, developers can focus on other intricate and imaginative parts of their work.

Better Compliance with Coding Standards

The automated code review process ensures that code conforms to coding standards and best practices. It allows code to be more readable, understandable, and maintainable. Hence, improving the code quality. Moreover, it enhances teamwork and collaboration among developers as all of them adhere to the same guidelines and consistency in the code review process.

Enhance Accuracy

The major disadvantage of manual code reviews is that they are prone to human errors and biases. It further increases other critical issues related to structural quality, architectural decisions or so which negatively impact the software application. Generative AI in code reviews can analyze code much faster and more consistently than humans. Hence, maintaining accuracy and reducing biases since they are entirely based on data.

Increase Scalability

When software projects grow in complexity and size, manual code reviews become increasingly time-consuming. It may also struggle to keep up with the scale of these codebases which further delay the code review process. As mentioned before, AI code review tools can handle large codebases in a fraction of a second and can help development teams maintain high standards of code quality and maintainability.  

How Typo Leverage Gen AI to Automate Code Reviews?

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

Auto fixing 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.

Comparing Typo with Other AI Code Review Tools

There are other popular AI code review tools available in the market. Let’s compare how we stack against others:





Code analysis

AI analysis and static code analysis




Code context

Deep understanding




Proprietary models





Auto debugging

Automated debugging with detailed explanations




Auto pull request

Automated pull requests and fixes




AI vs. Humans: The Future of Code Reviews?

AI code review tools are becoming increasingly popular. One question that has been on everyone’s mind is whether these AI code review tools will take away developers’ jobs.

The answer is NO.

Generative AI in code reviews is designed to enhance and streamline the development process. It lets the developers automate the repetitive and time-consuming tasks and focus on other core aspects of software applications. Moreover, human judgment, creativity, and domain knowledge are crucial for software development that AI cannot fully replicate.

While these tools excel at certain tasks like analyzing codebase, identifying code patterns, and software testing, they still cannot fully understand complex business requirements, and user needs, or make subjective decisions.

As a result, the combination of AI code review tools and developers’ intervention is an effective approach to ensure high-quality code.


The tech industry is demanding. The software engineering team needs to stay ahead of the industry trends. New AI tools and technologies can help them complement their skills and expertise and make their task easier.

AI in the code review process offers remarkable benefits including reducing human error and consistent accuracy. But, make sure that they are here to assist you in your task, not your whole strategy or replace you.


How Generative AI Is Revolutionising Developer Productivity

Generative AI has become a transformative force in the tech world. And it isn’t going to stop anytime soon! It will continue to have a major impact, especially in the software development industry.Generative AI, when used in the right way, can help developers in saving their time and effort. It allows them to focus on core tasks and upskilling. It further helps streamline various stages of SDLC and improves Developer Productivity. In this article, let’s dive deeper into how generative AI can positively impact developer productivity.

What is Generative AI?

Generative AI is a category of AI models and tools that are designed to create new content, images, videos, text, music, or code. It uses various techniques including neural networks and deep learning algorithms to generate new content.Generative artificial intelligence holds a great advantage for software developers in improving their productivity. It not only improves code quality and delivers better products and services but also allows them to stay ahead of their competitors.Below are a few benefits of Generative AI:

Increases Efficiency

With the help of Generative AI, developers can automate tasks that are either repetitive or don’t require much attention. This saves a lot of time and energy and allows developers to be more productive and efficient in their work. Hence, they can focus on more complex and critical aspects of the software without constantly stressing about other work.

Improves Quality

Generative AI can help in minimizing errors and address potential issues early. When they are set as per the coding standards, it can contribute to more effective coding reviews. This increases the code quality and decreases costly downtime and data loss.

Helps in Learning and Assisting with Work

Generative AI can assist developers by analyzing and generating examples of well-structured code, providing suggestions for refactoring, generating code snippets, and detecting blind spots. This further helps developers in upskilling and gaining knowledge about their tasks.

Cost Savings

Integrating generative AI tools can reduce costs. It enables developers to use existing codebases effectively and complete projects faster even with shorter teams. Generative AI can streamline the stages of the software development life cycle and get the most out of less budget.

Predict Analytics

Generative AI can help in detecting potential issues in the early stages by analyzing historical data. It can also make predictions about future trends. This allows developers to make informed decisions about their projects, streamline their workflow, and hence, deliver high-quality products and services.

How does Generative AI Help Software Developers?

Below are four key areas in which Generative AI can be a great asset to software developers:

It Eliminates Manual and Repetitive Tasks

Generative AI can take up the manual and routine tasks of software development teams. A few of them are test automation, completing coding statements, writing documentation, and so on. Developers can provide the prompt to Generative AI i.e. information regarding their code and documentation that adheres to the best practices. And it can generate the required content accordingly. It minimizes human errors and increases accuracy.This increases the creativity and problem-solving skills of developers. It further lets them focus more on solving complex business challenges and fast-track new software capabilities. Hence, it helps in faster delivery of products and services to end users.

It Helps Developers to Tackle New Challenges

When developers face any challenges or obstacles in their projects, they can turn to these AI tools to seek assistance. These AI tools can track performance, provide feedback, offer predictions, and find the optimal path to complete tasks. By providing the right and clear prompts, these tools can provide problem-specific recommendations and proven solutions.This prevents developers from being stressed out with certain tasks. Rather, they can use their time and energy for other important tasks or can take breaks.It increases their productivity and performance. Hence, improves the overall developer experience.

It Helps in Creating the First Draft of the Code

With the help of generative artificial intelligence, developers can get helpful code suggestions and generate initial drafts. It can be done by entering the prompt in a separate window or within the IDE that helps in developing the software.This prevents developers from entering into a slump and getting in the flow sooner. Besides this, these AI tools can also assist in root cause analysis and generate new system designs. Hence, it allows developers to reflect on code at a higher and more abstract level and focus more on what they want to build.

It Helps in Making Changes to Existing Code Faster

Generative AI can accelerate updates to existing code faster. Developers simply have to provide the criteria for the same and the AI tool can proceed further. It usually includes those tasks that get sidelined due to workload and lack of time. For example, Refactoring existing code further helps in making small changes and improving code readability and performance.As a result, developers can focus on high-level design and critical decision-making without worrying much about existing tasks.

How does Generative AI Improve Developer Productivity?

Below are a few ways in which Generative AI can have a positive impact on developer productivity:

Focus on Meaningful Tasks

As Generative AI tools take up tedious and repetitive tasks, they allow developers to give their time and energy to meaningful activities. This avoids distractions and prevents them from stress and burnout. Hence, it increases their productivity and positively impacts the overall developer experience.

Assist in their Learning Graph

Generative AI lets developers be less dependent on their seniors and co-workers. Since they can gain practical insights and examples from these AI tools. It allows them to enter their flow state faster and reduces their stress level.

Assist in Pair Programming

Through Generative AI, developers can collaborate with other developers easily. These AI tools help in providing intelligent suggestions and feedback during coding sessions. This stimulates discussion between them and leads to better and more creative solutions.

Increase the Pace of Software Development

Generative AI helps in the continuous delivery of products and services and drives business strategy. It addresses potential issues in the early stages and provides suggestions for improvements. Hence, it not only accelerates the phases of SDLC but improves overall quality as well.

5 top Generative AI Tools for Software Developers


Typo auto-analyzes your code and pull requests to find issues and suggests auto-fixes before getting merged.

Use Case

The code review process is time-consuming. Typo enables developers to find issues as soon as PR is raised and shows alerts within the git account. It gives you a detailed summary of security, vulnerability, and performance issues. To streamline the whole process, it suggests auto-fixes and best practices to move things faster and better.

Github Copilot

Github Copilot is an AI pair programmer that provides autocomplete style suggestions to your code.

Use Case

Coding is an integral part of your software development project. However, when done manually, takes a lot of effort. Github Copilot picks suggestions from your current or related code files and lets you test and select your code to perform different actions. It also ensures that vulnerable coding patterns are filtered out and blocks problematic public code suggestions.


Tabnine is an AI-powered code completion tool that uses deep learning to suggest code as you type.

Use Case

Writing code can prevent you from focusing on other core activities. Tabnine can provide accurate suggestions over time as per your coding habits and personalize code too. It also includes programming languages such as Javascript and Python and integrates them with popular IDEs for speedy setup and reduced context switching.


ChatGPT is a language model developed by OpenAI to understand prompts and generate human-like texts.

Use Case

Developers need to brainstorm ideas and get feedback on their projects. This is when ChatGPT comes to their rescue. This AI tool helps in finding answers to their coding, technical documentation, programming concepts and much more quickly. It uses natural language to understand questions and provide relevant suggestions.


Mintlify is an AI-powered documentation writer that allows developers to quickly and accurately generate code documentation.

Use Case

Code documentation can be a tedious process. Mintlify can analyze code, quickly understand complicated functions, and include built-in analytics to help developers understand how users engage with the documentation. It also has a Mintlify chat that reads documents and answers user questions instantly.

How to Mitigate Risks Associated with Generative AI?

No matter how effective Generative AI is becoming nowadays, it also comes with a lot of defects and errors. They are not always correct hence, human review is important after giving certain tasks to AI tools.Below are a few ways you can reduce risks related to Generative AI:

Implement Quality Control Practices

Develop guidelines and policies to address ethical challenges such as fairness, privacy, transparency, and accuracy of software development projects. Make sure to monitor a system that tracks model accuracy, performance metrics, and potential biases.

Provide Generative AI Training

Offer mentorship and training regarding Generative AI. This will increase AI literacy across departments and mitigate the risk. Help them know how to effectively utilize these tools and know their capabilities and limitations.

Understand AI is an Assistant, Not a Replacement

Make your developers understand that these generative tools should be viewed as assistants only. Encourage collaboration between these tools and human operators to leverage the strength of AI.


In a nutshell, Generative AI stands as a game-changer in the software development industry. When they are harnessed effectively, they can bring a multitude of benefits to the table. However, ensure that your developers approach the integration of Generative AI with caution.

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