Do AI Code Review Tools Like CodeRabbitAI Really Help Software Developers? Case Study

Want to speed up your software development workflow? Tired of waiting days for code reviews? Explore how AI agents for code reviews promise faster feedback, but do they really deliver? This in-depth review dives into a real-world test of CodeRabbitAI on actual projects, revealing where it shines and where it falls short. Learn how AI-assisted code review tools can impact your efficiency.

AI in Software Development: Skepticism vs. Pragmatism

Many software developers are wary of AI-generated code, especially in mission-critical systems. The non-deterministic nature of AI can lead to unpredictable results, and over-reliance can erode developers' core skills. Are AI code review assistants different? We examine how AI can support developers without replacing them.

AI Support Agents: The Code Review Revolution?

Imagine an AI colleague who reviews your code instantly, providing feedback, suggestions, and improvements right after you open a pull request. It's like having an always-available senior developer. This approach differs from AI code generation by keeping the problem-solving process in the developer's hands, improving code quality more organically. Learn how you can iterate on reviews faster using AI-powered code analysis.

CodeRabbitAI: A Hands-On Experiment

CodeRabbitAI, powered by the Claude model, was chosen for its free trial and GitHub integration. This tool was put to the test on two personal projects written in Lua:

• A NeoVim plugin providing a color scheme.
• A command execution engine.

Why Lua? To see how the AI copes with a less common language.

Initial Impressions: Summaries, Walkthroughs, and Linting

The initial pull request, a simple fix to the NeoVim plugin, revealed mixed results. The AI generated a summary and walkthrough (which proved somewhat confusing on small changes). It also missed the fact that it should have been two PRs. Despite these initial shortcomings, CodeRabbitAI had one unexpected benefit, it immediately added linting feedback through in-line comments.

Key Takeaways from PR #1:

• Speed: Near-instant feedback (30-60 seconds).
• Over-verbosity: Explanations were excessive for simple changes.

Diving Deeper: A More Complex Pull Request with CodeRabbitAI

A more complex pull request was tested on the command execution engine. The results were compelling.

Sequence Diagrams: Visualizing Code Flow

CodeRabbitAI generated sequence diagrams illustrating the control flow of the modified code! These diagrams, while not highlighting the specific changes, provide excellent documentation for understanding complex logic. For new team members, such visualizations can be invaluable.

Meaningful Comments: Catching Potential Issues

Beyond diagrams, CodeRabbitAI identified a missing dependency: a shell used in a test configuration that was not explicitly listed. Although not a critical issue, it demonstrated the AI's ability to find non-obvious problems.

ChatOps and Misunderstandings

Using CodeRabbitAI's ChatOps feature led to mixed results. Generating a PR title resulted in a generic, uninformative title. Even worse, requesting docstrings led the AI to add docstrings to all functions in the affected files, including existing ones, decorated with emojis. 🤦

Learning by Trial and Error

Fixing a small typo through CodeRabbitAI involved a series of missteps, including targeting the wrong branch. This highlighted the importance of providing specific instructions to the AI. The tool also lacked the capacity to execute common local Git commands.

The code review was not of the same quality as a senior engineer familiar with the project. For testing purposes I intentionally added a feature that already exists in the project. CodeRabbitAI failed to detect that the logic already exists, meaning such AI agents are still not capable replacing humans with nuanced understanding of a project’s history.

Dashboards and Analytics

AI code review tools offer valuable analytics. CodeRabbitAI provides dashboards showing usage patterns and uncovered errors. While a short trial period limited the data, the capabilities were sufficient to gauge adoption quality. The reporting feature allows for generating natural language reports of software changes, useful for product owners.

Final Verdict: Augment, Don't Replace

Although CodeRabbitAI has limitations, it should be used as an addition to human reviewers. The instant feedback and verbose walkthroughs (for larger PRs) are definitely a bonus. Even if the AI does little more than execute linters in the background, having the output explained in natural language directly in the PRs is not to be underestimated.

Embrace AI agents for code review to improve workflow but understand its limitations.