What is AI code review?

AI code review involves the use of artificial intelligence tools and techniques to assist in evaluating code for quality, style and functionality. The automated process employs machine learning models to identify inconsistencies with coding standards and detect security issues and vulnerabilities.

AI code review tools often provide suggestions or even automated fixes, helping developers save time and improve code quality. They can be embedded into integrated development environments (IDEs) and version control systems to facilitate continuous integration and continuous delivery (CI/CD) practices.

Code review is an essential part of the software development process. Developers within a software engineering team review each other’s work, proposing ways to strengthen the readability and maintainability of their code. This helps boost code quality and cultivates a culture of collaboration and continuous improvement.

Generative AI can automate and enhance the mechanics of traditional code reviews. It can swiftly tackle huge volumes of code and constant streams of pull requests (PRs), flagging subtle bugs that human reviewers might overlook. Developers can spend more time and effort fixing issues instead of finding flaws.

Most AI code review systems are powered by large language models (LLMs) trained on source code. They help turn manual reviews into automated workflows.

The AI code review process typically follows these steps:

• Training models

• Understanding semantics

• Analyzing code

• Generating suggestions

• Adapting to user feedback

LLMs are trained on vast codebases written in various programming languages. These models learn to recognize patterns in code that might signal issues or inefficiencies. The training process equips models with the capability to distinguish between good and bad coding practices, so they can spot them in new code they encounter.

Generative AI models also draw on natural language processing techniques to parse different code elements, such as function definitions, inline comments and variable names, and determine the relationships between them to glean intent. This allows LLMs to go beyond syntax, understanding the meaning behind complex code changes and dependencies to identify more nuanced issues and generate suggested fixes that likely align with the code’s expected behavior or purpose.

AI-assisted code review can be part of continuous integration pipelines, with webhooks set up to automatically trigger the process when a developer commits their code changes, opens a new pull request or updates an existing PR. AI code review tools then examine the diff (which highlights code changes, much like the Track Changes feature in Microsoft Word or the Suggesting mode in Google Docs) and the surrounding code. Some tools provide IDE extensions or plugins for real-time analysis as developers write code, so they stay in flow.

Many AI-powered code review platforms conduct static code analysis first. This entails running linters that detect formatting and styling issues or automated scanners that inspect repositories for potential bugs, code smells and security issues. Static code analysis tools often rely on predefined rules for their checks, and their results establish a baseline for AI code review.

After analysis, AI code review tools pinpoint issues and produce recommendations for fixes and improvements. They usually offer explanations behind their suggestions.

LLMs often have mechanisms to adapt and improve based on user feedback. Developers can approve and implement AI-generated recommendations or reject them, helping models refine their understanding and enhance their outputs over time.

AI-assisted code review offers these benefits for an organization and its development team:

• Better error detection

• Consistency

• Efficiency

• Enhanced developer productivity

AI-powered code review tools are highly effective at spotting subtle bugs that might be missed through manual reviews. And with some systems pinpointing issues in real time, error detection becomes a proactive rather than a reactive effort.

Long queues of PR reviews, rapid releases and navigating large codebases can contribute to fatigue for developers, which can lead to inconsistent reviews. AI can analyze code no matter the volume, helping implement consistent coding style, standards and best practices across the entire repository.

AI-driven code reviews help shorten development cycles. An hour-long bug-hunting mission for a human reviewer can take just minutes for AI-driven tools, which can in turn result in faster feedback loops and more time spent on remediating rather than identifying issues. This “shift-left” approach catches problems before they reach a pull request, reducing rework and preventing costly incidents that propagate into production.

With AI tools automating repetitive checks, developers can focus their energy on solving more complex tasks. These tools also allow human reviewers to zero in on architecture, design, edge cases and security considerations instead of syntactical issues.

Here are some challenges software development teams might encounter when integrating AI into the code review process:

• False positives and negatives

• Limitations in understanding context

• Overreliance

AI code review systems can generate false positives, incorrectly flagging code as problematic, or false negatives, missing actual flaws. These inaccuracies can complicate the code review process, leading to wasted time on unnecessary fixes or unaddressed issues that contribute to increased technical debt.

Supplying custom rules that outline accepted patterns can help prevent false positives. Testing, including unit testing, static application security testing and dynamic application security testing, remain crucial to catch any missed errors.

Developers must treat the outputs of AI code review tools as proposals that still require human verification. Teams must also monitor the performance of these tools and consider retraining them if they continue to produce high false positive and negative rates.

AI tools often struggle with the specific context of a project, including the intricacies of APIs and frameworks, complex business logic, domain-specific scenarios, edge cases and overall architecture. This lack of contextual understanding can lead to inadequate validation of code quality and missed opportunities for optimizations.

Fine-tuning on an enterprise’s own codebase can help improve context awareness and domain-specific reasoning. Teams can also consider using retrieval-augmented generation (RAG) or the Model Context Protocol (MCP) for AI agents. RAG and MCP allow models to connect to and access relevant files and up-to-date resources—such as API and technical documentation, business logic specifications, secure coding standards and software architecture diagrams and flowcharts—that can provide additional context for AI code reviews.

Developers might become overly dependent on AI tools for streamlining code review processes, leading to a diminished emphasis on professional expertise and critical thinking. This reliance can result in unchecked technical debt, as developers overlook deeper issues that require human oversight.

A way to overcome this challenge is to put clear policies in place for the responsible use of AI in code reviews. An organization must set boundaries to prevent any misuse and try to strike a balance between quality and speed. Teams must also bear in mind that the human element is still the most important factor in code review and AI is only augmenting the process.

AI code review can significantly enhance the software development process by helping teams maintain high code quality and efficiency. Here are a few general steps to effectively integrate AI-driven tools into a business’s code review workflow:

1. Choose the right AI code review tool: To start, select an AI code review tool that fits the organization’s needs. Many of the popular options offer various features, including support for multiple programming languages and integration with existing workflows. Organizations must look for tools that provide metrics to assess code quality, such as code complexity, duplication rates and adherence to coding standards. These metrics help an organization set benchmarks for its code review process.
    

2. Set up onboarding and configuration: Once a tool has been chosen, the next step is onboarding the team. This requires clear documentation and training sessions to familiarize everyone with the tool’s features and capabilities. Organizations need to configure tools to align with coding standards and specific project requirements, which might include setting up custom rules or thresholds for specific metrics.
    

3. Incorporate AI in the review process: The next step is integrating the AI tool into the organization’s existing code review process. The AI generates review comments based on its analysis, highlighting potential issues and suggesting improvements. This process will not only streamline the review process but will also allow developers to learn from feedback over time.
    

4. Use metrics to drive improvements: Organizations can take the information from the AI code review and use those metrics to track a team’s performance. By monitoring trends in code quality over time, development teams can point to bottlenecks and areas of improvement. Teams can also use these insights to generate ideas for how to address recurring issues and improve coding practices.
    

5. Balance AI and human insights: AI-driven code review tools can vastly improve the code review process, but it’s essential to balance automated feedback with human insights. Organizations must encourage team members to review AI-generated feedback and provide their own perspectives. This collaborative approach can bolster the review process and foster a culture of learning and continuous improvement from team members.

Options abound for AI-powered code review tools. Here are a few popular choices:

• Claude Code

• Codacy

• CodeRabbit

• Cursor

• GitHub Copilot

• IBM Bob

Anthropic’s Claude Code has a Code Review feature that dispatches a fleet of AI agents for automated PR reviews. The agents work in parallel to search for bugs, verify them to filter out false positives and rank them according to severity. Checks focus on correctness but can be expanded by adding guidance files to repositories.

The Codacy platform offers a hybrid code review engine that blends deterministic code analysis with context-aware AI reasoning to detect business logic gaps, duplicated code, overly complex functions and security vulnerabilities. It supports custom instructions and Jira integration for added context. Codacy’s AI Reviewer is currently available only on GitHub.

This AI platform orchestrates an entire system for code reviews:

• A sandboxed execution environment composed of an enterprise’s cloned repository, a graph representation of code dependencies, linters and security scanners for isolated, multidimensional code analysis

• Context enrichment through MCP servers, linked issues in project management systems and a knowledge base of a company’s code review instructions and coding guidelines

• AI agents for review, verification, chat and pre-merge checks

CodeRabbit classifies its findings as either a potential issue, a code refactoring suggestion or a minor formatting or style improvement. It also assigns different severity levels for its findings.

CodeRabbit supports programming languages such as C++, Java, JavaScript, Python, Ruby and TypeScript.

Cursor’s Bugbot agent automatically reviews pull requests and proposes fixes. Teams can define coding standards and custom rules and set up project-specific guidelines. Bugbot integrates with both GitHub and GitLab.

The Copilot code review feature conducts PR reviews and suggests fixes. Teams can tailor reviews through custom instructions stored as one or more files in their repositories. These instructions contain natural language statements and other information for Copilot to consider when reviewing code. Copilot code review is supported throughout the GitHub ecosystem and in VS Code and JetBrains IDEs.

IBM Bob is an AI-powered development partner that augments existing workflows and supports developers as they reason about code and make decisions. Bob offers a code review feature that analyzes code changes, validates issue coverage and flags potential issues before developers commit their work.

Code reviews can be configured and initiated using chat commands or a dedicated review panel. The panel provides a visual interface with two review modes: a branch comparison mode to compare code changes against any branch and an issue coverage mode to validate local changes against a specific GitHub issue. While Bob can work with GitHub and GitLab for branch comparisons, issue coverage validation is currently supported only on GitHub.