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Evaluate AI-generated code the way you would any proposed software change: check that it meets the requirement, behaves correctly, fits the project, and is safe to accept. Combine builds and tests with security analysis, dependency review, and human judgment; a passing test suite or an AI assistant’s own review is not enough to establish that a change is ready.
1. Establish what the change is supposed to do
Start with the request, acceptance criteria, and surrounding code—not with the generated explanation. Identify which files and behaviors should change, then compare the implementation with the project’s architecture, conventions, and business rules. GitHub’s guidance on reviewing AI-generated code likewise emphasizes checking the change against its intended task and project context.
- Confirm that the code addresses the actual user or system need, rather than only a plausible interpretation of the prompt.
- Check assumptions about inputs, permissions, user behavior, and business rules against existing requirements or established behavior.
- Inspect unrelated edits, deleted code, and changed or removed tests. Determine why each change is necessary.
- Look for missing parts of the requested behavior, including error handling and interactions with neighboring components.
Tests can show that specified cases behave as expected; they cannot tell you whether the implementation solves the right problem if the requirement or test coverage is incomplete.
2. Check build, behavior, and test coverage
Run the project’s normal build or compile checks, then the tests relevant to the changed code. Review new warnings and errors rather than treating a successful command as the end of review. Add or request tests for important behavior that is not exercised by the existing suite.
#1 Best Overall
- Expected behavior: Does the change produce the required result for ordinary, valid inputs?
- Failure cases: Does it handle invalid input, unavailable services, permission failures, and other errors appropriately?
- Edge conditions: Check relevant boundaries, empty or unusually large values, and state transitions.
- Regression risk: Does existing behavior remain intact where the change is not meant to alter it?
A passing suite is evidence only for the behaviors its tests actually exercise. Read the tests as well as the implementation: confirm that assertions meaningfully verify the requirement, and that tests were not weakened or removed to make the change pass.
3. Review security using checks suited to the risk
No single scan can establish that a change is secure. The National Institute of Standards and Technology’s developer-verification guidance describes complementary verification methods, including design-level threat modeling, automated tests, static analysis, secret checks, fuzzing, and dependency checks. Choose methods according to the application, the change, and the consequences of failure.
Rank #2
- Threat model the design: Consider what the change exposes, who can reach it, what data or privileges it handles, and how it could be misused. This can reveal design flaws that a code scanner cannot infer.
- Inspect code and scan it: Use static analysis and targeted code review to look for unsafe data handling, authorization gaps, injection risks, and other issues relevant to the language and application.
- Check for secrets: Run suitable secret-detection checks and inspect changed files for credentials or sensitive configuration that should not be committed.
- Test adversarial and unusual inputs: Use fuzzing or other robustness tests where appropriate; use web-application scanners when the change affects a web application.
- Verify coverage of known risks: Consider historical test cases and code-based or black-box structural tests that target relevant security properties.
- Assess included services and libraries: Review new or changed packages and services as part of the security decision.
Automated findings need interpretation: investigate whether an alert applies to the changed code, and do not treat a clean scan as proof that the design and behavior are safe.
4. Verify dependency and supply-chain changes
Generated code may introduce a package or alter a lockfile even when its explanation focuses on application logic. Review the actual dependency diff, not just the assistant’s summary. For each new package, verify that it exists, is maintained, comes from a credible source, and has a license compatible with the project. Check whether a dependency is genuinely needed and whether the change brings in additional transitive packages.
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Automated checks can catch some defects, but maintainability still requires a reviewer to understand the code in context. Read the implementation as if another developer will need to debug, test, or extend it later.
- Are names, comments, and structure clear and consistent with the surrounding project?
- Does the code use established project patterns, or introduce a new abstraction without a clear benefit?
- Can the behavior be tested and changed without unnecessary coupling or duplication?
- Would a smaller or simpler implementation be easier to understand while meeting the same requirements?
Comments should clarify decisions or non-obvious behavior, not merely restate what the code already says. Prefer a design that makes its assumptions and failure paths understandable to future maintainers.
Rank #4
6. Compare implementations on the same basis
When choosing between an AI-generated implementation and another proposed fix, evaluate both against the same requirements and test conditions. There is no universal numeric score established by the cited guidance; a weighted-looking score can obscure a serious security or correctness failure. Compare the concrete dimensions instead:
| Review dimension | What to compare |
|---|---|
| Functional behavior | Whether each implementation meets the same requirements, including relevant failure cases and edge conditions. |
| Security | Risks identified and whether appropriate design review, tests, scanning, and other checks cover them. |
| Dependencies | Packages, provenance, maintenance, licensing, and the impact of dependency changes. |
| Maintainability | Clarity, consistency with project patterns, testability, and expected effort to understand or change the code. |
7. Require accountable human approval
Before merge or deployment, a human developer must own the decision. OWASP’s Secure Coding with AI guidance calls for explicit developer review and approval, with a human responsible for correctness, security, and maintenance. An AI assistant’s self-review can help surface questions, but it does not transfer that responsibility. Keep authorship, review, and approval clear in the team’s normal workflow.
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