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September 20, 2023
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min read

Quality Assurance using AI Code Reviews

As software developers and data scientists, we have had whole weeks burnt on codebase maintenance. We've had bugs in our codebase that date 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, AI code reviews and 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 reviews and refactoring benefit our developer journey.

Firstly, what are AI code reviews and refactoring? Think ChatGPT, but bred specifically to analyze and generate code. AI code reviews & refactoring use neural networks to restructure existing code without changing its external behavior. Problematic code is detected without executing code and changes are made to the internal structure, improving quality, clarity, and maintainability at the same time. By training on industry-standard code and past code modifications, AI made for code reviews and 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 for code reviews and 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 who 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 code review 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 AI code reviews & refactoring?

  1. Minimized Disruption

    Tools (like Metabob) that utilize AI for code reviews and refactoring can be integrated into your workflow. With a simple extension, such tools can be applied to your entire codebase to enhance code quality, maintainability, and security.

  2. Long-term value

    Investing in an AI code review & refactoring tool pays off in the long run. The AI would grow along with your team, learning your coding etiquette and improving its accuracy. You would save time and resources while ensuring the maintainability of your software.

  3. Customizability

    AI code review models can be customized to your specific use case. Using enriched industry-specific data or even your teams’ internal datasets, the AI can learn to detect problems that are specific to your teams’ use cases.

Metabob is an AI code review 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. Try the tool now!


Product Manager

Axel is an expert in product management. He has his hand in every team making sure Metabob runs like a well oiled machine.