A Developer's Guide to AI Code Review Tools

According to a GitLab survey, 60% of developers say code reviews are "very valuable" for ensuring code quality and security. Though they play a significant role in code quality, code reviews require developer time.

By using AI and automation, you can significantly reduce the developers' resources spent on code reviews, while still catching and fixing issues effectively. AI code review tools not only speed things up but also work with precision, learning from massive datasets and understanding your codebase before making suggestions.

If you're here to improve your code review process, I'll share some tools based on my research and experience. I'll also explain how to choose the right one and integrate it seamlessly.

What Are AI Code Review Tools and How Do They Work?

Code reviews are a major part of coding tasks that can be automated with AI. Because they involve less complex architecture decisions and more about identifying issues or ineffective code practices, AI is mostly used here.

Before a human developer approves your PR, the AI performs an initial scan to detect coding errors, bad practices, missing test cases, and potential fixes.

The advantage lies in its speed and scalability. AI can analyze vast amounts of code in seconds, spotting patterns and anomalies much faster than a human ever could.

Best AI Code Review Tools Compared

1. CodeRabbit

CodeRabbit provides context-aware suggestions on your pull requests within seconds. It connects with GitHub and other Git platforms, integrates with IDEs like Visual Studio Code, and runs real-time analysis on pull requests.

CodeRabbit stands out for its real-time collaboration features. Once a PR opens, it launches a full AI-powered review and delivers actionable feedback instantly.

CodeRabbit is adaptive, meaning it continuously learns from your team's coding practices and offers personalized suggestions over time.

Key features:

• CodeRabbit reports highlight trends like recurring issues, review turnaround times, and quality scores.
• You can set up custom review instructions that CodeRabbit follows.
• You can use @coderabbit mentions with natural language instructions to perform certain tasks on PRs. For example, @coderabbitai pause stops ongoing review.

2. Tembo

Tembo isn't your typical code review assistant that just suggests improvements. It acts as an autonomous software engineer that continuously identifies issues and creates fixes. Instead of stepping in after you raise a PR, Tembo lives in your codebase, monitors your development environment, and proactively resolves errors.

By the time you're ready to raise a PR, most of your code issues are already fixed. Tembo also integrates seamlessly with specialized code review tools like CodeRabbit.

When CodeRabbit suggests improvements on your PR, Tembo reads those suggestions, implements the fixes, and creates a new PR for you to review and approve.

Key features:

• Agentic AI solution that can perform end-to-end tasks.
• Integrates with Sentry for error monitoring.
• Seamlessly connects with CodeRabbit to implement its code review suggestions.
• Monitors PostgreSQL databases and generates optimization recommendations for indexes, queries, and schema.

3. DeepSource

DeepSource is a unified DevSecOps platform that combines SAST, static code analysis, and code coverage into a single solution.

The tool scans your PRs and flags issues ranging from code smells to security vulnerabilities.

It runs in-depth static analysis on every PR to identify potential problems in your codebase without executing the code. The good part is that it assigns a severity level to each issue. You can use these scores to prioritize and fix the most critical ones first.

Another interesting feature is that it can group similar issues together. Through this, you can tackle the identical issues in batches instead of one at a time.

Key features:

• DeepSource can automatically create and assign issues to team members for critical problems.
• It provides detailed reports with visual insights into code health and security trends.
• You can write your own set of rules to detect issues not covered by the built-in ones, such as architectural constraints, business logic violations, or company-specific coding standards.
• In your repository dashboard on the DeepSource UI, some issues will have the Autofix™ option, and when you click it, the tool applies automatic fixes for them.

4. Codacy

Codacy is an AI-driven code review tool that automates code reviews, both pre-commit and post-commit. It uses static analysis with a rule-based approach to identify issues such as code smells or security vulnerabilities.

While Codacy doesn't automatically fix issues, it effectively detects them and provides clear, recommended fixes.

Codacy lets you choose from 150+ code quality rules across 20+ languages to customize analysis for your needs.

It also categorizes issues into several groups, helping you prioritize tasks or assign them to the right team members. The main categories include Code Style, Error Prone, Performance, Security, Compatibility, Code Complexity, Documentation, and Unused Code.

Key features:

• Codacy error categorization helps you prioritize issues from critical categories first.
• Codacy monitors how much of the code is tested and provides code coverage insights.
• Its reports visually answer questions like What's the state of your project's code quality?, What's the overall test coverage?, and What's the code complexity percentage?
• The best part is that Codacy is free for open-source projects and small teams. Larger teams and enterprises pay for advanced features like SAML/SSO support, audit trails, and custom rulesets.

5. Copilot for Pull Requests

If you're already using AI code assistants, you probably know that GitHub Copilot is one of the top tools helping developers write code. It also provides intelligent PR review suggestions.

Since Microsoft and OpenAI back the tool, Copilot is one of the most widely adopted tools in the industry. Developers who already use GitHub Copilot as their coding assistant will find it effortless to use for PR reviews as well.

Key features:

• Copilot proposes PR recommendations with proper descriptions, giving developers greater context and clarity on each suggestion.
• It highlights key changes and dependencies within PRs, so developers easily know the exact changes and make more confident merge decisions.
• Marker tags: Developers can insert marker tags in pull request descriptions. Copilot expands on the mentioned marker tag. For instance, when you add a marker tag copilot:summary, Copilot expands the PR changes and suggestions into a detailed and contextually relevant summary.
• Copilot Gentest: Copilot identifies changes in your PR that lack tests and suggests relevant tests.

Benefits and Limitations of Using AI Code Review Tools

Benefits of code review tools

Increased productivity: Acting as an initial filter, AI tools scan your code, detect syntax errors, identify potential bugs, and suggest optimizations. This allows teams to focus on more complex problems that truly require human insight.

Consistency: By automatically enforcing coding standards and style across projects, AI tools eliminate discrepancies caused by subjective human evaluations.

Round-the-clock availability: After raising a PR, you don't need to wait for your senior developer slot to review them. The tool kicks in immediately after the code push and provides instant feedback.

Early detection of security issues: In addition to code quality checks and error detection, AI tools also scan for security vulnerabilities that human reviewers might overlook.

Personalization: AI code review tools can learn from your codebase style and become familiar with your team's coding conventions, preferred libraries, and architectural patterns. This enables them to provide personalized and context-aware suggestions.

Limitations of code review tools

Lacks business knowledge: While AI excels at analyzing code syntax and common programming patterns, it doesn't truly understand the unique business logic or intent behind your code.

Privacy concerns: Many AI code review tools send code snippets to cloud servers for analysis. This raises concerns about data leaks or unauthorized sharing, especially when you are using sensitive data. In those cases, it's safer to use on-premise deployment tools or proprietary software.

Poor training data: If the training data includes outdated coding practices or biased records, it may provide incorrect suggestions. This becomes especially problematic for teams working with niche technologies or specialized frameworks that the AI training data has less visibility into.

How to Choose the Right AI Code Review Tool for Your Use Case

Diff coverage

Diff coverage isn't the same as test coverage. The best AI code review tools should specialize in reviewing pull requests rather than the entire codebase.

They should focus on detecting changes within a PR and suggesting tests for any uncovered modifications.
This targeted approach gives more attention to new or updated code without wasting time on areas that haven't changed.

Seamless integration into your stack

You've likely already built your coding ecosystem, and you can't change all of it just to enable AI code review. So, choose a code review tool that fits naturally into your ecosystem — whether that's GitHub, GitLab, Bitbucket, or a self-hosted CI/CD pipeline.

Privacy risks

Cloud-based code review tools often send your data to external servers for context and processing, which can increase the risk of data leakage or exposure.

While this may not be a major concern for less critical data, it becomes risky when dealing with proprietary or sensitive information.

In such cases, consider tools that offer on-premise deployment with strict security guardrails. For example, you can deploy SonarQube on-prem within your CI/CD pipeline to provide secure, in-house feedback during PRs.

Scalability & performance

Some tools perform well on small to medium-sized codebases but struggle with larger monorepositories.

If you handle large and complex projects, evaluate the tool's accuracy and performance to ensure it doesn't slow down your CI/CD pipeline.

The tool should also handle concurrent PRs efficiently, since complex monorepos often have multiple teams working simultaneously. For this, test its performance by monitoring time taken, resource usage, concurrency, and accuracy.

Integrating an AI Code Review Tool Into Your Development Process

AI code review tools typically activate right after you raise or update a PR. They automatically scan your PR and provide actionable suggestions. The depth and type of feedback depend on the tool you choose.

For instance, GitHub Copilot accurately detects issues and provides clear recommendations, while tools like CodeRabbit go a step further by including code snippets for simple fixes. If you're looking for more autonomous capabilities, you can combine CodeRabbit with Tembo.

How does this integration work? As soon as you raise a PR, CodeRabbit reviews it and provides the necessary suggestions. Then Tembo reads those code review comments, implements the changes, and automatically raises a new PR with the fixes.

You just need to review Tembo's PR and approve it. Beyond code reviews, this setup automates the fixes using Tembo's autonomous capabilities.

How to integrate these into your workflow? This guide provides step-by-step instructions to set up CodeRabbit and Tembo in your GitHub ecosystem.

Conclusion

Code reviews are essential for maintaining quality and error-free code. However, they take significant developer time. When done right with AI, much of this process can be automated.

An AI code review tool can read your PR, suggest improvements, and flag issues without requiring a human reviewer.

With Agentic AI, it goes even further — it automatically implements the fixes. The developer's job is just to validate the AI PRs and approve them.

To enable this Agentic AI-driven automation in your coding workflows, sign up for Tembo today!

FAQs About AI for Programming

Can AI code review tools replace human reviewers?

AI code review tools replace certain tasks of human reviewers rather than fully replacing them. They automate identifying issues and fixing them. The final call to merge the code is always with humans.

How do I measure the impact of an AI code review tool in my team?

You can measure the impact by tracking code quality improvements, conducting developer surveys, and calculating developer productivity metrics such as AI suggestion acceptance rate and the number of issues reaching production.

You can also run A/B tests — give one team access to AI code review tools and have another continue with traditional workflows. Compare the results across these metrics to evaluate the impact.

What risks should I watch for when adopting AI for code review?

These are the common risks to keep in mind:

• If the training data contains bias, you would see inaccurate suggestions.
• Check whether the AI's recommendations introduce any security vulnerabilities.
• It can produce hallucinatory results.
• Monitor if it increases the complexity of code snippets.
• There's also a risk of sensitive data exposure.