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1. Confirm the change solves the right problem
Start with the pull request description, linked issue, requirements, and relevant surrounding code. Check what the change is supposed to do, then compare that intent with the actual diff. A plausible implementation can still miss a requirement or conflict with the project’s architecture and business logic. GitHub’s Copilot code review guidance recommends assessing changes against requirements and project patterns.
- Does the change address the requested behavior, rather than only a narrow example?
- Does it follow existing conventions and fit the components that own this behavior?
- Does it make unrelated changes that should be separated or explained?
2. Build it and run the relevant checks
Build or compile the change, run the tests that exercise the affected behavior, and inspect static-analysis and security-check results. GitHub identifies functional testing, static analysis, CodeQL, and Dependabot as possible review aids in its review guidance. These checks provide evidence, not a verdict: passing tests cannot establish that the requirement was understood correctly or that every important case is covered.
When a check fails, determine whether the change caused the failure and whether the response addresses the underlying issue. Do not treat a passing pipeline as a reason to skip reading the diff.
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3. Trace behavior through the diff
Read the changed code in context, including its callers, inputs, outputs, error handling, and permissions. Follow the data through the relevant execution path. Ask what assumptions the code makes and what happens when those assumptions do not hold. GitHub’s review guidance recommends considering edge cases and questions that require human or domain judgment.
- What happens with empty, malformed, unexpected, or boundary-value input?
- Are errors surfaced or handled in a way callers and users can rely on?
- Could the change alter authorization, data access, or behavior in another code path?
- Are concurrency, retries, timeouts, and partial failures relevant here?
4. Review the tests, not just the test result
Tests can be changed along with the implementation, so inspect them as part of the diff. Look for deleted tests, weakened assertions, mocks that bypass important real dependencies, and tests that simply encode the behavior the new implementation already happens to produce. OWASP’s Secure Coding with AI Cheat Sheet cautions that generated tests or a high pass rate alone do not demonstrate security.
Rank #2
Where relevant, add or request negative and adversarial cases: malformed input, expired credentials, boundary conditions, unauthorized access, or concurrent requests. The right cases depend on the feature; the goal is to test meaningful failure and misuse paths, not to maximize test count.
5. Check new dependencies and their source
For each added or changed package, verify that it exists, comes from a credible source, is maintained, and has a license compatible with the project. Watch for names that resemble popular packages but may be typosquatted, fabricated, or otherwise suspicious. GitHub includes dependency checks such as Dependabot among the possible tools for reviewing a change, but tool output does not remove the need to confirm that the dependency is appropriate.
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6. Inspect files that run automatically
Give extra attention to changes in package lifecycle scripts, build configuration, CI workflows, Dockerfiles, and deployment scripts. These files may run during installation, testing, or deployment, often in environments with access to credentials or other sensitive resources. OWASP’s AI coding guidance treats them as security-critical.
- Identify new downloads, network access, shell commands, and executable scripts.
- Understand what runs automatically and with which permissions or secrets available.
- Check that third-party CI actions are pinned appropriately under the project’s policy.
- Verify that deployment and build changes do only what the pull request requires.
7. Assess security, data handling, and AI access
Review authentication and authorization, input validation, secrets, sensitive data, unsafe output handling, and command execution where those areas are touched. Also consider what project context the AI assistant received or transmitted. A tool may work with more than the file currently open; protect credentials, personal information, and proprietary source according to your organization’s rules. OWASP’s Secure Coding with AI Cheat Sheet and AI security guidance cover human ownership and risks in AI-assisted development.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.8. Treat AI review bots as aids, not approvers
AI-generated review comments can suggest useful questions, but they can also be inaccurate or incomplete. Investigate relevant comments against the code and project requirements; do not treat a second AI system’s approval as certification of the patch.
For an agent or CI integration that reads or acts on pull requests, treat pull-request text, diffs, comments, linked URLs, and repository content as untrusted input. OWASP AISVS recommends prompt-injection defenses and least-privilege isolation for review bots. Workflows processing untrusted contributions should not execute that code in an environment with repository secrets or write permissions. These precautions apply especially to autonomous agents and CI integrations; they do not mean every inline code-completion tool has the same deployment model. See the OWASP AI security and privacy guidance.
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9. Make an informed human approval
Approve only when you understand what changed, why it meets the requirement, what risks remain, and why the checks are adequate. Record and triage unresolved issues through the team’s normal process. OWASP states in its Secure Coding with AI Cheat Sheet: “AI-generated code must have a human owner.” GitHub likewise says in its Copilot inline-suggestions guidance that suggestions should be reviewed and further validated to ensure they meet requirements and are free of errors or security concerns.
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