Review AI-generated code as you would any proposed software change: check that it meets the actual requirements, behaves as expected, fits the project, and does not introduce unacceptable security or maintenance risks. Tests and automated scanners help, but they cannot replace a human review of intent, design, dependencies, and trade-offs. Use the workflow below before approving a change.
1. Confirm what the change is supposed to do
Start with the request, acceptance criteria, and surrounding code—not the AI’s description of its own output. Compare the change with the intended behavior, the application’s architecture, and established project conventions. Check assumptions about business rules and how users will interact with the feature.
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- Identify which files and behaviors the request should affect, then look for unrelated changes.
- Inspect edits to tests, configuration, and error handling. Understand why any existing test or code was changed or removed.
- Check whether the implementation solves the requested problem in this project, rather than merely producing plausible code.
GitHub’s guidance on reviewing AI-generated code emphasizes checking the result against the task and its context; passing tests alone does not establish that the right problem was solved: GitHub Docs: Review AI-generated code.
2. Verify behavior and correctness
Build or compile the change, run relevant tests, and inspect new warnings and errors. Then assess whether the tests cover the behavior the request requires, including failure cases and meaningful edge conditions.
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- Check expected inputs and outputs, validation, and error paths.
- Consider boundary conditions and unusual but valid inputs that could expose incorrect assumptions.
- Look for missing tests, tests that assert too little, or tests weakened to make the change pass.
A passing test suite is evidence only for the behavior it exercises. It is not proof that the whole change is correct, complete, or aligned with the requirement.
3. Review security using checks matched to the risk
Security review should combine methods rather than rely on a single scan. Choose checks based on the application, the code’s exposure, and the consequences of failure. NIST’s developer-verification guidance describes complementary approaches, including design-level threat modeling, automated testing, static code analysis, secret checks, structural tests, fuzzing, web application scanners where applicable, and review of included libraries, packages, and services: NIST: Guidelines on Minimum Standards for Developer Verification of Software.
- Think through threats: Identify what data or operations the change touches, who could misuse them, and where trust boundaries or authorization checks matter.
- Use automated checks: Run relevant static analysis and security tests; check for accidentally embedded credentials or other secrets.
- Test beyond the happy path: Use fuzzing or black-box and code-based structural tests when they fit the component and its risk.
- Check the whole integration: Review affected services, libraries, and packages, not only the newly generated function.
A clean automated report does not settle design questions or establish that every relevant weakness has been tested. Read findings in context and investigate what the chosen checks do not cover.
4. Inspect dependencies and supply-chain changes
Review the actual dependency and lockfile diff. For each new package, verify that it exists, is maintained, comes from a credible source, and has a license compatible with the project. Do not accept a package merely because generated code names it or explains its use.
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5. Judge maintainability and project fit
Readability and long-term maintenance need human judgment. Check naming, structure, comments, and consistency with nearby code. Ask whether another developer can understand the implementation, test it, and safely change it later. If a smaller or simpler solution would be clearer, prefer that over unnecessary complexity.
6. Compare alternatives on the same basis
When evaluating two implementations or proposed fixes, use the same requirements and test conditions for each. Compare the dimensions that matter to the project:
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| Review dimension | What to compare |
|---|---|
| Behavior | Whether each implementation meets the requirements, including relevant failure cases and edge conditions. |
| Security | Risks introduced and the coverage of checks appropriate to those risks. |
| Dependencies | New packages, their provenance and maintenance, and licensing impact. |
| Maintainability | Readability, consistency with the project, and the expected effort to test and change the code. |
These dimensions support a reasoned review, not a universal numeric score. A single score can conceal a serious weakness in one area behind strengths in another.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Keep approval accountable
An AI assistant’s explanation or self-review does not transfer responsibility. OWASP’s guidance calls for a human owner for AI-assisted changes and explicit developer review and approval before merge or deployment: OWASP: Secure Coding with AI. Make the reviewer and approval clear in the team’s normal workflow, and base approval on the code and evidence—not on the fact that an AI produced it.
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