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 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:
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:
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 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 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, 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, 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.
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
Autofixing the codebase with an option to directly create a Pull Request
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.
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.
Typo uses optimized practices and built-in methods spanning multiple languages. Hence, reducing code complexity and ensuring thorough quality assurance throughout the development process.
Typo standardizes code and reduces the risk of a security breach.
Click here to know more about our Code Review tool
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.
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.
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.
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.
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