First, it wrote the code. Now, it’s reviewing it too.
AI is changing how development teams build and ship software. Engineers no longer need to rely on manual oversight alone—today’s tech scans code for bugs, flags style violations, and suggests fixes within seconds of a commit. AI-powered systems act more like assistive copilots than replacements, functioning as intelligent pull request (PR) agents with accuracy and consistency.
But like any reviewer, AI doesn’t always get it right.
In this guide, we’ll explain how AI code reviews work, what makes them effective, and where they sometimes miss the mark. You’ll learn about their key components, benefits, and limitations—and explore the top tools driving the shift in how teams manage code quality.
What Are AI Code Reviews?
AI code reviews use machine learning (ML) and natural language processing (NLP) to analyze code quality, flag bugs, enforce style rules, and identify potential security vulnerabilities. Trained on massive datasets that span programming languages and frameworks, these systems spot things that traditional tools might miss, from logic errors and code smells to risky patterns. Using AI for code reviews allows teams to catch these issues earlier, often before changes reach a PR.
These systems plug directly into integrated development environments (IDEs) and version control platforms like GitHub, where AI-powered suggestions appear in real time. This automation streamlines the review process without disrupting the workflow, reducing repetitive tasks and supporting safe, maintainable codebases.
However, AI code review still requires human oversight, as the risks of AI in software development are constantly evolving. Not every suggestion will be accurate, which is why experienced code reviewers must interpret results with sound judgment.
AI Code Review Key Components
AI code reviews blend several components, each adding context, structure, or precision to how the review happens. Here’s what drives most AI-assisted code reviews under the hood.
Static Code Analysis
This technique scans code without running it to catch bugs, security flaws, and style violations early in the process. It’s fast and scalable—AI can analyze thousands of lines in seconds, making it especially useful for large, complex codebases.
Security teams often include this AI code check as part of broader scanning strategies, like static application security testing (SAST), to identify vulnerabilities before code hits production. The results feed into AI models that refine feedback over time, which means static checks grow smarter with every review.
Dynamic Code Analysis
Where static checks stop, dynamic ones begin. This method runs the application to see how it behaves in real time, catching runtime errors, performance issues, and vulnerabilities that only appear when the system is live. Often used alongside static scans, it gives a more complete picture of how code performs under real conditions.
Rule-Based Systems
Rule-based systems apply predefined logic to enforce coding standards and flag violations. Linters that spot formatting issues or enforce naming conventions are an example of this logic. In AI-based code review, these systems flag issues like style inconsistencies or code smells—so teams can implement standards without combing through every line manually.
Natural Language Processing (NLP)
NLP enables AI to interpret both code and human-written inputs, including commit messages and documentation. Trained on large datasets, NLP models improve over time, allowing AI to distinguish between a harmless code quirk and a potentially dangerous oversight.
Large Language Models (LLMs)
With LLMs such as GPT-4, AI code reviews go beyond basic pattern matching like spotting naming errors or formatting issues. These models understand complex logic, like edge cases, conditionals, and nested loops, and generate review comments in clear, conversational language. And by communicating in a way developers easily understand, LLMs facilitate better collaboration and deeper insight into the reasoning behind each suggestion.
How AI Code Review Tools Work: 6 Steps
AI code review tools plug into developer workflows and provide intelligent, context-aware feedback. Here’s what typically happens behind the scenes during an AI code review.
1. A Developer Opens or Updates a PR
Most tools connect to platforms like GitLab or Bitbucket using webhooks configured across multiple repositories. When code is pushed or a PR changes, the webhook fires an event that delivers metadata and a snapshot of the changes to the tool.
2. The Code Is Cloned and Parsed
The tool fetches the code—sometimes just the diff, other times the full codebase—and parses it using an abstract syntax tree (AST), a structural map that breaks the code into its logical components. This gives the system an understanding of how developers structured and assembled the code.
3. Static Checks Are Performed
Most tools run static analysis and linting to flag syntax errors, formatting problems, and stylistic inconsistencies before handing the code to the AI. These early scans surface issues like unused variables or inconsistent indentation, setting the baseline for deeper, context-aware analysis.
4. AI Models Analyze the Changes in Context
Code segments are sent to LLMs trained on massive codebases. Traditional security code review tools tend to rely on rule-based scanning, which is effective for known patterns but limited in scope. LLMs, by contrast, analyze a wider context to uncover security flaws and logic gaps that static tools might overlook.
5. Suggestions Are Generated and Surfaced in the Pull Request
The AI generates human-like review comments or line-by-line recommendations to create a summary report for code reviewers. The tool posts these comments directly to the “Files changed” tab of the PR, just like notes from a real person.
6. Developers Apply Feedback and the Cycle Continues
After reviewing suggestions, developers can apply changes, leave responses, or push new commits. Over time, models adapt based on developer feedback, allowing them to take a faster and more context-aware approach to traditional manual code audits.
Benefits of AI Code Review
When used well, AI strengthens the entire development process and workflow. Here’s what teams can gain from integrating AI into their code review process:
- Accelerates development without sacrificing quality: AI tools dramatically reduce review time by automating routine checks. Developers can catch syntax issues, flag architectural concerns, and resolve bugs before merging code into the main branch.
- Standardizes code quality across teams: AI-powered systems apply consistent styling and structural choices to every PR. This eliminates ambiguity and replaces subjective feedback with straightforward, rule-backed suggestions.
- Uncovers hard-to-spot errors: AI models excel at detecting subtle logic bugs, edge case handling issues, and performance bottlenecks that might slip past manual reviews. They also recognize risky patterns that could lead to future defects.
- Enhances security posture: Many AI tools learn to spot insecure coding practices, enabling developers to fix input validation problems and outdated libraries. As part of a broader set of AI cybersecurity tools, they strengthen code-level defenses early in the software development lifecycle (SDLC). And in the SDLC, earlier is always better.
Limitations of AI Code Review
AI might accelerate code reviews, but it still has blind spots. Despite rapid progress, there are still places where these tools fall short:
- Lacks context and intent awareness: AI can review syntax, structure, and patterns, but it doesn’t understand the business logic behind the code. That makes it harder for the AI tool to catch issues tied to specific features or user needs.
- Can’t always separate signal from noise: False positives waste developer time. False negatives let real issues slip through. Without regular fine-tuning or human feedback, some tools become noisy sidekicks rather than reliable reviewers.
- Struggles with complex logic: AI isn’t great at reasoning through multistep algorithms or creative problem-solving paths. In some cases, it flags clean code or misses subtle flaws altogether, especially in larger, more intricate codebases.
- Risk of overreliance: It’s tempting to let AI handle everything, but that leads to weaker instincts and less engaged teams. Over time, developers might stop questioning suggestions or lose confidence in their ability to debug complex problems. And that, in turn, undermines long-term growth and collaboration in the development process.
Top 5 AI Code Review Tools
The AI code review tools market has no shortage of options, but a few platforms consistently rise above the rest. Here’s a closer look at some of the top platforms that are changing how teams review code.
1. Legit Security
Legit Security brings security context to AI code reviews by continuously monitoring the software supply chain. Instead of checking for syntax or formatting, Legit flags risky behaviors like leaked secrets, suspicious contributor activity, and tampered dependencies that might go unnoticed.
These insights surface directly in PRs, allowing security and development teams to focus on real threats without slowing down the pace of delivery. With AI-backed risk scoring and behavioral analysis, Legit makes it easier to catch issues earlier in the SDLC and improve review hygiene across fast-moving pipelines.
2. Swimm
Swimm works inside your IDE and acts as a documentation engine that gives helpful context during AI code review. It draws from team-written docs, past commits, and inline explanations, which allows developers to ramp up faster and make quicker review decisions.
Unique to Swimm is its “/ask” feature, which works like a code-aware assistant. Developers can ask about confusing logic and get answers that clarify the purpose behind the code—no need to decode legacy patterns.
3. Codacy
Codacy automates code reviews across more than 30 programming languages, with checks for style, duplication, and common security flaws. The platform connects directly to GitHub, GitLab, and Bitbucket to fit seamlessly into everyday developer workflows, but its team-wide customization options are what set it apart—you can enforce quality standards across projects and track progress with visual dashboards that highlight code health trends.
4. DeepCode
Powered by machine learning that’s trained on millions of open-source repos, DeepCode offers precise suggestions based on real-world patterns. It catches subtle issues that human reviewers might overlook, especially in complex or deeply nested code. DeepCode’s strength lies in its speed and breadth. It supports multiple languages and provides real-time feedback as you write, not just after a PR.
6. Code Climate
Code Climate zeroes in on long-term maintainability. Beyond flagging bugs and duplication, it scores your code’s health so that your team can spot tech debt before it snowballs. The platform delivers actionable insights for simplifying code and improving architecture, making it ideal for teams focused on scalable, sustainable development.
Protect Your SDLC With Legit Security’s AI-Native ASPM
AI is revolutionizing how developers write code, but it also introduces new risks, from malicious AI-generated code to subtle security gaps that traditional review tools often miss. Legit Security’s AI-native ASPM platform continuously monitors your SDLC and catches what other providers overlook. It analyzes commits, contributors, pipelines, and dependencies to reveal how code moves through your system. Legit also flags threats like leaked secrets and unauthorized code changes before they reach production.
By integrating directly into your development workflows, Legit adds a security layer that keeps pace with the speed of modern software teams. It doesn’t just scan code—it tracks who wrote it and whether it violates your governance or compliance rules. With continuous visibility, you can scale AI-assisted development without compromising security.
Upgrade your AI code review strategy with SDLC-wide protection from Legit Security. Book a demo today.