How to Effectively Use AI Code Reviewers on GitHub

Code review is a critical step in the software development lifecycle. However, it’s not always easy to encounter well-structured code during the review process, especially when dealing with large repositories containing numerous files and folders. As your project grows over time, more files are added to accommodate new features. But introducing new features is only part of the challenge-ensuring that the code behind those features meets your project’s standards is equally important.

With multiple software engineers collaborating on a single project, each contributing their unique coding style, it becomes crucial to review the code to ensure adherence to established standards and protocols, especially in open-source environments. Code reviews ensure the code is well-documented, easy to understand, and follows proper structure using classes and functions. If the code modifies the existing codebase, the changes must meet the requirements and be thoroughly tested. Every detail matters during a code review, and this process demands significant attention to detail.

Code reviews, while essential, often turn into a tedious and time-consuming process. This is where AI tools come in, offering much-needed efficiency and support. A range of advanced tools now integrates seamlessly with GitHub, simplifying the process. In this blog post, we’ll focus on one such AI-powered solution: Qodo Merge. We’ll walk you through the tool step by step, demonstrating how to use it effectively to streamline your GitHub review code workflow.

What is an AI Code Reviewer?

An AI code review tool is designed to automatically analyze code changes, provide detailed feedback, and assist developers in enhancing their code quality using large language models.

These tools offer significant advantages in software development:

• Detecting potential bugs and security vulnerabilities early to ensure a secure and stable codebase.
• Providing instant, actionable feedback to streamline the review process.
• Suggesting code optimizations for cleaner, more efficient code.
• Reducing manual review time by automating repetitive tasks.

Deep Dive: Qodo Merge (PR-Agent) Features

Qodo Merge, an open-source GitHub AI code review tool, sets a new standard in AI-assisted reviews. This innovative tool offers a variety of features that simplify and enhance the code review process.

Auto-Description

Qodo Merge automatically generates comprehensive PR (pull request) descriptions to save developers time and effort. These descriptions include:

• PR name and type: A clear, concise identification of the pull request.
• Detailed code walkthrough: An automated explanation of changes within the code.
• Key change summaries: Highlights of the most significant updates.

PR Review Capabilities

The tool delivers in-depth analysis by:

• Identifying the PR’s main theme: Pinpointing the primary focus of the code changes.
• Flagging potential security issues: Highlighting vulnerabilities or compliance risks.
• Offering targeted improvement suggestions: Recommending changes to enhance code quality.

Advanced Interaction Commands

Developers can interact seamlessly with Qodo Merge using intuitive commands in their GitHub workflow:

• /describe: Instantly generates a detailed PR description.
• /review: Triggers a comprehensive analysis of the pull request.
• /ask: Enables developers to pose specific questions about the PR for clarity.
• /improve: Offers specific suggestions for improving code quality.

Note: You can learn about more commands that this tool provides in the Qodo Merge documentation. In addition, you can use the ‘/help’ command in the comment section to get direct access to all the commands.

Let’s look at these commands one by one in more detail. This will help us understand how AI code reviewers can reduce our efforts. I am using a pull request from an open-source GitHub project, ‘deepgaze,’ as an example, which involves the addition of unit tests for a file. The project is related to computer vision and works on images to perform functions such as face detection. The screenshot below also includes the user’s description:

The ‘describe’ Command

Let’s start with the describe command before proceeding to review the PR using Qodo Merge. We will simply use the command ‘@CodiumAI-Agent /describe’ in the comment section to get the description:

At a glance, you can see that the Qodo Merge tool provides a summary that includes the title, user description, PR type, detailed description, and a walkthrough of the changes. This is impressive because the AI generates an automated result containing all the relevant details for the PR, without manually reviewing the entire PR and the changed files.

In addition to the title and user description, the agent identifies the PR type. It then provides an in-depth description of the PR. The description is as follows:

• Added comprehensive test cases for the ‘DiffMotionDetector’ class in the ‘deep gaze’ module.
• Implemented tests to verify successful setting and retrieval of background images.
• Tested the return of binary images after the detection process.
• Included tests to handle None inputs for both background and foreground images.

The description is precise, covering information about the class where test cases have been added. It provides an overview of the test cases, which verify the successful setting and retrieval of background images. It then moves to the next details, including how the unit test checks the binary images returned after the detection process implemented in the new code, and finally outlines the included tests.

Let’s now expand the ‘changes walkthrough’ section. This section contains the changes made to the base code. Here, it mentions the changes in the ‘test.py’ file:

Notably, the AI-generated description is far more comprehensive than the user-provided description.

The ‘review’ Command

Let’s now move on to the next command, which is to review the PR. The command goes like this: ‘@CodiumAI-Agent /review’, and below, you can see  how the tool helps you increase efficiency by focusing on the important things:

The review highlights key observations made by the AI. It provides an effort estimate, indicating the complexity of the PR. It then identifies what the PR is related to-in this case, mentioning that it pertains to test cases. Importantly, it addresses a critical question: whether the PR contains any security concerns.

For instance, if you are building an AI chatbot using OpenAI’s API key and accidentally leave your key in the code without removing it, the AI tool can help identify this potential risk. Additionally, it can detect whether a PR contains any malicious code that could harm your entire codebase, especially in an open-source project, by introducing viruses. This feature is invaluable for preventing malicious attacks on your base code and identifying potential security threats.

Finally, it points out the focus area for the review. In this case, it mentions that there are redundant tests for setting and retrieving a ‘None’ background image. It suggests combining these tests or eliminating unnecessary repetitions to make the test suite more efficient.

The ‘ask’ Command

The ask command can be used like this: ‘@CodiumAI-Agent /ask Does this code follow project coding standards and best practices?’ You can type a question related to your concern by adding the question after the ‘/ask’. Let’s see what the tool generates for us:

When we asked the question, “Does this code follow project coding standards and best practices?” The tool generated a detailed answer consisting of seven different points.

Let’s just summarize the answer: The test class ‘TestDiffMotionDetector’ is well-structured, with clear method names and appropriate use of assertions, ensuring good readability and maintainability. However, handling external resources like images could be improved by mocking or using in-memory alternatives for robustness. The inclusion of tests for ‘None’ values ensures better input handling. Minor improvements, such as adding a new line at the end of the file and grouping imports clearly, would align with best practices. Overall, the tests are solid but could benefit from these refinements.

The ‘improve’ Command

The improve command is my favorite because it can provide incredible improvements that enhance both the functionality and quality of the code. Our PR will look much better with these enhancements, don’t you think? Here, let’s take a look at one such example:

The tool generates great suggestions for improving our PR code. In this case, it recommends adding error handling for missing or unreadable image files. The suggestion is to validate the image paths before using them. Not only does it provide suggestions, but it also gives us the exact code we can use to enhance our PR.

The best part of this tool is that it’s not only limited to GitHub code reviews but also supports AI code reviews for GitLab.

Impact of AI on Open-Source Software (OSS) Project Maintenance

AI code reviewers are revolutionizing open-source project management by addressing critical challenges in collaborative software development. These tools tackle complex problems that often strain volunteer-driven projects:

• Managing large volumes of contributions efficiently.
• Maintaining consistent code quality across diverse contributor bases.
• Identifying potential risks before they escalate.
• Enabling maintainers to focus on strategic development rather than repetitive tasks.

The real power of AI in OSS lies not just in technical analysis but in its ability to democratize code quality. By providing instant, objective feedback, AI tools help bridge skill gaps and support less experienced contributors in producing high-quality code.

Best Practices for Using AI Code Reviewers

To maximize the potential of AI-powered code review tools, developers and project maintainers should:

• Integrate strategically: Position AI as a collaborative tool, not a replacement for human expertise
• Maintain human oversight: Always validate AI suggestions against project-specific requirements
• Customize extensively: Configure tools to align with unique project coding standards
• Iterate and improve: Regularly update and refine AI tool configurations

Conclusion

AI code reviewers represent a significant advancement in software development, offering unprecedented insights and efficiency. Tools like Qodo Merge are transforming how teams approach code quality, enabling more collaborative, intelligent, and streamlined development processes.

However, it’s crucial to recognize that these tools are not without limitations. While powerful, AI code reviewers cannot completely replace human judgment. They may struggle with complex, context-specific architectural decisions and require careful oversight to ensure they align with project-specific requirements.

By thoughtfully integrating AI-powered review tools-understanding both their strengths and limitations-organizations can unlock new levels of productivity, code quality, and collaborative potential. The key lies in viewing AI as a powerful assistant that complements, rather than replaces, human expertise.