Verify an AI code review finding by turning it into a testable claim, checking the code path and requirements, then running the cheapest check that could confirm or refute it. Treat the reviewer’s explanation as a hypothesis—not evidence—and keep a human responsible for the merge decision.
Use a short verification loop
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Turn the comment into a testable claim
Write down the changed location, the condition that would trigger the alleged defect, and the consequence. For example: “When an unauthenticated request reaches this handler, it can read another user’s record.” A comment that names a suspicious pattern but cannot explain a plausible path to impact is unproven.
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Trace the code in context
Read the diff, then follow the relevant caller and callee paths. Check nearby input validation, authorization, configuration, and the project’s stated requirements. A fragment that looks unsafe in isolation may be protected by a check elsewhere, or may conflict with the change’s intended behavior. GitHub’s code-review guidance recommends considering purpose, architecture, and project conventions.
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Run the cheapest decisive check
For a functional claim, start with a focused existing unit or integration test. If none covers the path, add a small test that asserts the behavior that matters. For a security claim, use a safe local test, a relevant static-analysis rule, or an isolated reproduction where possible. GitHub recommends running tests and static analysis early; OWASP’s AI security guidance identifies automated security-testing categories for pull requests containing AI-generated code.
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Get independent evidence when the impact matters
Reproduce the behavior without relying on the reviewing model’s narrative. Compare the observed output, state change, trace, or test result with the claim. A persuasive explanation can help you find what to inspect, but it cannot establish that the described behavior occurs.
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Record a decision and its basis
Confirm the finding with a minimal reproducer, failing test, trace, or independent corroboration. Dismiss it with a concise explanation tied to code or requirements. If the evidence is insufficient, leave it unresolved and escalate it rather than treating uncertainty as a pass. Keep a short record of the claim, check, result, and owner.
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Prioritize by impact and evidence
Review first the findings that point to a concrete changed line and describe a credible route to user-visible failure, data exposure, authorization bypass, or another security consequence. Then consider lower-impact maintainability or style comments. A model’s severity label is a prioritization hint, not evidence that the issue exists.
Match the check to the claim. A focused test can exercise a functional path; a dependency finding calls for inspection of the declaration, actual use, and relevant advisory context; a security claim calls for tracing untrusted input toward a sensitive operation and checking whether any guard is present and effective.
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| Check | Best fit | What it establishes | What remains uncertain |
|---|---|---|---|
| Focused unit or integration test | A specific functional path or expected property | Whether the tested scenario produces the asserted result | Behavior outside the exercised path and untested requirements |
| Static analysis or scanner | Known vulnerability patterns, dependencies, secrets, or configuration, depending on the tool | That a configured rule matched the code or artifact | Whether the path is reachable and the impact is real; rule coverage is limited |
| Local or isolated reproduction | A concrete behavior or security claim that can be exercised safely | Observed behavior under the reproduction’s conditions | Conditions not reproduced, including other environments or paths |
| Model explanation alone | Generating a hypothesis or suggesting where to inspect | No independent confirmation of the claim | The claim’s accuracy and applicability to this codebase |
GitHub’s review guide names CodeQL for vulnerabilities and Dependabot for dependency issues. OWASP’s guidance lists SAST, IAST, DAST, secret scanning, infrastructure-as-code scanning, and software composition analysis among security-testing categories for AI-generated-code pull requests. Use checks that fit the repository and claim; no single scanner validates every behavior or design decision.
Watch for AI-specific failure modes
- Invented APIs: Confirm that a cited function, parameter, or library behavior exists in the project’s actual version.
- Missed constraints: Check the proposed concern against project requirements, conventions, and architectural intent.
- Misread surrounding controls: Follow the path far enough to find validation or authorization checks outside the flagged fragment.
- Tests that avoid rather than fix the problem: Check whether a test was deleted, skipped, or weakened instead of correcting the behavior.
GitHub’s Copilot inline-suggestions responsible-use documentation says: “Hallucinations are a known risk of large language models and are a key reason that human review of AI-generated output is important.” A passing test suite is useful evidence for the behavior it covers, not proof that every finding outside that coverage is false. Similarly, a scanner alert shows that a rule matched; reachability and impact still need review.
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Keep a human accountable for the merge
OWASP’s Secure Coding with AI Cheat Sheet says AI-assisted changes should be reviewed, approved, and attributable to a developer responsible for security and maintainability; it advises against deploying AI-generated code without human review and approval. A second model or an automated scanner can help prioritize or corroborate a concern, but neither assumes responsibility for accepting the change.
Before approval, make sure the relevant claim has a reasoned disposition, the check and result are recorded where appropriate, and a named developer owns the decision. Verification is not a requirement to prove every speculative comment impossible; it is a requirement not to approve consequential changes on the strength of an AI explanation alone.
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