Review AI-generated code as an untrusted change: trace each edit to a requirement, inspect its behavior and security impact, run tests and analysis, then require a human to approve the final diff. Use AI review tools to surface issues, not to decide that code is safe to merge.
Why AI-Generated Code Needs a Different Review
Generated code can look consistent and still misunderstand a requirement, miss an edge case, or introduce an unsafe dependency or data flow. A passing test suite only shows that the code passed the checks you ran; it does not prove the tests cover the intended behavior. Review the actual diff and verify the assumptions behind it.
Use This Checklist Before Merge
- Set the review boundary. Identify the request the code is meant to satisfy, the files it changed, and any generated or unrelated edits. Ask the author or agent to explain the purpose of each non-obvious change.
- Trace changes to requirements. For each requirement, point to the code and a test or other observable check that demonstrates it. Mark requirements with no corresponding implementation or verification.
- Read the diff in context. Follow calls into surrounding code and check inputs, outputs, state changes, error handling, and boundary conditions. Look for changes that silently broaden permissions, expose data, or alter existing behavior.
- Challenge assumptions and edge cases. Check empty, malformed, duplicated, unusually large, and unauthorized inputs where relevant. Confirm that failures are handled safely and that retries or partial updates cannot leave inconsistent state.
- Inspect dependencies and copied material. Check new dependencies and unfamiliar code against your team’s approval and licensing process. A code similarity or license scan can flag potential issues, but interpret the result and resolve obligations through your normal process.
- Run the project’s checks. Run the relevant tests, static analysis, and security checks in the repository’s established workflow. Review failures and coverage gaps; do not treat a green result as proof that an untested requirement works.
- Review automated findings. For each finding, inspect the cited code and reproduce or reason through the issue. Record why a finding is valid, fixed, or safely dismissed so a later reviewer can follow the decision.
- Make a human merge decision. Confirm the diff still matches the request, required checks have completed, and unresolved risks have an owner and disposition. The person approving the change remains responsible for the merge decision.
Where These Tools Fit In The Review
These products cover different parts of the checklist. Select based on the gap in your workflow, and confirm vendor details for any integration, language, or deployment requirement that is not established here.
| Tool | Documented fit for this checklist | What to verify |
|---|---|---|
| Sourcery | Reviews pull requests with a summary, comments, and suggested fixes; it says it catches logic errors, missed edge cases, and security issues. It supports every programming language GitHub recognizes. The vendor says it keeps no copy of code after review and does not train AI on it. | Confirm the relevant repository host and plan. GitLab merge requests are supported on every plan; GitHub Enterprise Server is listed for the Enterprise plan. |
| Codeleaks | Checks code, whether human-written or AI-generated, for licensing concerns, with real-time license detection and repository-agnostic scanning. Its stated language examples include Ruby, JavaScript, TypeScript, Python, C++, C, Java, C#, and PHP. | Confirm whether your source and workflow are supported. The vendor states that every 250 tokens count as one credit; check the vendor site for applicable usage and licensing details. |
| CodeThreat | Analyzes pull request changes for security risks and can run a project-wide review. The vendor says it supports 27+ programming languages and frameworks and integrates with GitHub, GitLab, Bitbucket, CI/CD pipelines, and cloud providers. | Confirm the specific language, framework, repository setup, and checks your team needs. |
| Graphite | Offers AI reviews on pull requests to catch critical bugs and suggest fixes. Graphite Chat provides code-change context and help with CI failures from the pull request page. | The supplied details specify GitHub syncing; confirm that this matches your repository setup and required workflow. |
| Kiro | Its agents write code and run tests while the developer reviews and decides. You can inspect changes as they happen, approve or edit them, and configure project or global steering files with team constraints. | Confirm that its IDE workflow and your team’s verification needs fit the project. The vendor lists macOS, Windows, and Linux availability. |
| Parasoft Jtest | For Java, it provides static analysis, compliance checks, and AI-generated JUnit tests. The vendor describes using it in build pipelines to find and fix static-analysis violations and generate and execute tests. | Confirm the rules, pipeline behavior, and test coverage approach your team requires. |
| Qodo | Runs specialized agents on pull requests to surface bugs, rule violations, and requirement gaps with codebase context. It also describes enforceable rules and issue traceability. | Confirm the specific integrations and language coverage your review process requires. |
Protect Code And Review Data
Before sending repository content to a review service or enabling an agent, check the vendor’s current data handling, retention, training, access, and licensing terms, then compare them with your organization’s policy. Sourcery states that it retains no copy of code after review and does not train AI on it; that statement applies to Sourcery and should not be assumed for other products. Codeleaks describes preventing proprietary code from being stored in AI repositories and alerting users to potential licensing requirements. Check the vendor site for details that are not established here.
#1 Best Overall
Turn Findings Into A Repeatable Team Gate
Make the checklist part of the pull request routine: require a concise explanation of the change, link each requirement to verification, and record the disposition of material automated findings. Keep security, licensing, and behavior checks visible to the human reviewer. Choose tools only for checks they specifically document, and confirm unsupported details with the vendor before relying on them.
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