As software developers and data scientists, we have had whole weeks burnt on codebase maintenance. We've had bugs in our codebase that dated back to when we first made our first git project commit. We have come across many challenges that come with maintaining and improving complex codebases. Over time, our software systems accumulate technical debt, decreasing productivity, scalability, and maintainability.

Lo and behold, Generative AI Code Refactoring. It not only automates improvements on our code, but also provides us with quantitative and qualitative insights into our code quality. In this blog, we’ll dive into how AI code refactoring benefits our developer journey.

Firstly, what is Generative AI Code refactoring? Think ChatGPT, but bred specifically to analyze and generate code. Generative AI Code refactoring uses neural networks to restructure existing code without changing its external behavior. It makes changes to the internal structure, improving clarity and maintainability at the same time. By training on industry standard code and past code modifications, generative AI refactoring detects problematic code, improves code readability, reduces complexity, eliminates redundancy, and promotes extensibility.

How can we benefit from this?

1 | Improved Maintainability

We spend so much time debugging, or we make someone do it (sorry Kevin) and if you are Kevin (or you want to make Kevin’s life better), AI refactoring simplifies complex code and algorithms, making it easier to understand and maintain. By removing technical debt and code smells, we will spend less time troubleshooting and more time adding value through new features and optimizations.

2 | Enhanced Scalability

We’ve all had to retrace and rewrite code to make a new, important feature work. AI refactoring improves upon our code to lay a solid foundation for scalability. By replacing current complex code with simple, clean, and modular code, our software is more flexible and adaptable to growth and changing business needs.

3 | Increased Developer Productivity

We all know someone that loves to write complicated code (no naming names)… And we spend a lot of time understanding and debugging complex code. AI refactoring improves code readability, facilitating better collaboration between team members. It is now also easier to get the new guy up to speed and we can now produce high-quality code in much less time.

4 | Bug Prevention

Nobody likes bugs. AI refactoring identifies and wipes out potential bugs and vulnerabilities in the codebase. By eliminating code smells, enhancing error handling, and implementing industry best practices, we can reduce software malfunctions and breaches.

Why use a trusted Generative AI refactoring service?

1 | Expertise

A Generative AI refactoring service needs to be in the industry for some time to run a successful algorithm. Their team would possess in-depth expertise in various programming languages, algorithmic design, and best practices. Their refactoring service would analyze our codebase extensively and comprehensively.

2 | Objective Perspective

A third-party, Generative AI refactoring service would bring a fresh perspective to our codebase. It could identify hidden issues, suggest creative solutions, and provide valuable insights on improving code quality.

3 | Minimized Disruption

AI refactoring services can be integrated into your workflow. With a simple extension, code refactoring can be applied to our entire codebase.

4 | Long-term value

Investing in an AI refactoring service pays off in the long run. The AI would grow along with us, learning our coding etiquette and improving its accuracy. We would save time and resources while ensuring the maintainability of your software.

5 | Customizability

AI refactoring services typically come with parameters to tune for a more/less sensitive algorithm. The AI could also be customized to suit our coding requirements.

What is an example of a free-to-use Generative AI tool?

Metabob is a Generative AI refactoring tool that utilizes proprietary graph neural networks to detect problems and LLMs to explain and resolve them. Metabob’s Graph Neural Networks (GNNs) utilize an attention mechanism to comprehend both semantic & relational markers, resulting in a more complete representation of the input. Metabob’s Large Language Models (LLMs, such as GPT) are deep learning models that use billions of parameters & an attention mechanism to predict the most likely token to follow a given input.

Metabob offers free-to-use individual developer versions and is currently available on VScode as an extension. Do try them out over here Metabob: Debug and Refactor with AI - Visual Studio Marketplace.