Review AI-generated code against the behavior the change is supposed to deliver—not against the explanation or tests that came with it. Read the full diff and surrounding code, verify the intended behavior with relevant tests and checks, inspect test changes just as carefully as implementation changes, and require accountable human approval. AI authorship alone does not make code unsafe, but code and tests produced together are not independent evidence that the result is correct.
1. Establish the intended behavior and scope
Start with the issue, acceptance criteria, or user-visible requirement. Before judging whether the implementation looks plausible, define what it must do and what it must not change.
- Identify the expected inputs, outputs, state changes, and error behavior.
- Check whether the patch changes public interfaces, data contracts, permissions, or compatibility with existing callers.
- Decide whether the change is appropriately scoped or includes unrelated edits that need separate explanation.
- For design-level security consequences, use threat modeling rather than relying only on line-by-line review; NIST includes threat modeling among its software verification techniques (NIST, Guidelines on Minimum Standards for Developer Verification of Software).
If the requirement is unclear, resolve that ambiguity before treating a passing test or a persuasive code explanation as proof of correctness.
2. Read the full diff in context
Inspect every changed file, then follow the relevant code into its callers and dependencies. A locally reasonable change can still break a contract or bypass a check elsewhere in the system.
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- Trace data from entry point through validation, authorization, state changes, persistence, and output.
- Examine failure paths, boundary conditions, and concurrency or lifecycle assumptions where they apply.
- Review package and dependency changes, including lockfiles and newly introduced services.
- Give special scrutiny to build scripts, package lifecycle scripts, CI workflows, Docker or other build files, and deployment configuration. These files may execute in trusted contexts with elevated privileges; OWASP specifically flags generated changes to build and deployment files as security-sensitive (OWASP Secure Coding with AI Cheat Sheet).
Pay particular attention to authentication, authorization, input validation, and cryptographic behavior. These are areas where plausible-looking code can create serious consequences, and OWASP recommends manually authored tests for security-critical operations.
3. Verify behavior with independent evidence
Run the project’s existing focused tests and the broader relevant suite. Add cases derived from the requirement and realistic misuse—not merely tests that confirm what the generated implementation currently does.
Test the expected behavior and its failure modes
- Cover valid behavior as well as invalid inputs, malformed payloads, expired credentials, and boundary values where relevant.
- Exercise authorization failures and attempts to cross trust boundaries.
- Consider concurrency, retries, and lifecycle transitions when those affect the component.
- For security-sensitive behavior, write or review tests independently of the code-generation process.
A passing suite is meaningful only to the extent that its cases represent intended behavior and relevant failure modes. A test suite generated by the same agent as the implementation may inherit the same mistaken assumptions; OWASP states, “A passing test suite generated by the same agent that produced the code provides no independent assurance.”
Choose verification methods that fit the risk
Do not treat one test command as a universal safety gate. NIST’s developer-verification guidance describes a range of techniques: automated tests, static code scanning, heuristic secret detection, built-in protections, black-box and structural test cases, historical tests, fuzzing, web application scanners where applicable, and checks of included libraries, packages, and services. Select the methods that fit the system, change, and risk rather than assuming every technique applies to every patch.
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- Use static analysis, secret scanning, and dependency checks where available and appropriate.
- For exposed parsers or complex input handling, consider fuzzing; for web applications, consider applicable application scanners.
- Confirm that checks actually completed and examine failures rather than treating a green status summary as a substitute for evidence.
NIST published its verification guidance on October 6, 2021; it is a menu of verification methods, not a required sequence or a claim that any single tool catches every defect (NIST publication).
4. Audit the test diff separately
Tests are part of the change, not neutral proof supplied by it. Inspect additions, edits, and deletions with the same care as production code.
- Ask why each deleted or modified test was changed; compare the old and new assertions.
- Look for weakened expectations, such as broad success checks replacing precise results or errors.
- Check whether new mocks bypass the real dependency or behavior the test is meant to exercise.
- Reject tests that only assert the implementation’s observed output when that output has not been established as the required behavior.
- Look for missing negative and boundary cases, especially around permissions, validation, and sensitive operations.
A suite that passes after assertions have been loosened may provide less assurance than it did before. The key question is whether the changed tests still constrain the behavior the requirement calls for.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Treat AI review tools as an additional signal
An AI review assistant can surface a defect or suggest a useful question, but a human must evaluate its findings and remain accountable for approval. Before relying on a tool, confirm which files and languages it reviews, what it excludes, and how its results fit into your pull-request checks.
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For example, GitHub documents that Copilot code review excludes dependency-management files such as package.json and Gemfile.lock, as well as log and SVG files. Feature availability and configuration depend on plan and organization settings, so verify the current coverage for your repository rather than assuming all changed files were reviewed (GitHub Docs: About GitHub Copilot code review).
GitHub also documents repository-wide and path-specific review instructions. Its documentation describes Copilot approvals as a configurable feature that has been in public preview; check current organization settings and policy before treating an AI approval as a merge requirement (GitHub Docs: Using GitHub Copilot code review). Review comments and automated approvals do not replace accountable human review.
6. Make a traceable merge decision
Before merging, confirm that the change matches the requirement, relevant checks completed, test changes are justified, and findings are either resolved or explicitly accepted under team policy. Obtain approval from an appropriate human reviewer. Escalate testing and review for high-impact or security-critical changes according to your team’s risk policy, and record material assumptions or accepted residual risk.
There is no universal approval count or severity threshold that fits every repository. The merge decision should make clear what evidence was considered and who accepted any remaining risk.
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