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Treat an AI-generated fix as an untrusted proposed change, not as a patch that is safe because it looks plausible or CI is green. Before merging, establish the defect and expected behavior, inspect the complete diff, run tests that independently check the requirement, examine security and dependency risks, and get qualified human approval. If material edits follow approval, review the new diff and rerun presubmit checks.
The goal is not to prove that a tool never makes mistakes. It is to make the change understandable, testable, and accountable enough that the people approving it can explain what it does and why its risks are acceptable.
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Start with the problem, not the generated implementation. A reviewer needs a clear account of the original defect, the intended behavior, and the boundaries the change must preserve. Without that baseline, it is difficult to tell whether the patch fixes the bug, hides its symptoms, or creates a different failure.
Reproduce the defect and define the acceptance criteria
When possible, reproduce the failure against the unmodified code. Record the relevant inputs and conditions, the actual result, and the expected result. Then express the acceptance criteria in observable terms: for example, which input should now succeed or fail, what response should be returned, or which state must remain unchanged. Do not use the generated patch itself as the definition of success.
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If the failure cannot be reproduced, say why and identify the evidence that supports the report. A clear explanation of the defect still matters: it lets a reviewer assess whether the implementation addresses the cause rather than adding a workaround that only fits one example.
Write down the invariant and the risk boundary
An invariant is something the change must not break. Depending on the code, that may be an authorization check, a data-retention rule, an API contract, a transaction boundary, or a guarantee that a failed operation does not partially update state. Mark security and data-handling boundaries explicitly. The reviewer should be able to explain both why the patch addresses the defect and what is intended to remain unchanged.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsKeep the scope narrow enough to review. If the proposed fix also changes architecture, formatting, configuration, or unrelated behavior, identify the reason for each change. A patch can contain valid improvements and still be too broad to assess confidently as one change.
How do you review the complete diff?
Read every changed file, not only the main source file or the summary written by the person or tool that produced the patch. The review unit includes tests, lockfiles, build and deployment configuration, scripts, and workflows because those files can change what executes or what gets shipped.
Review file categories that are easy to overlook
- Application code: Trace the changed logic through its callers, error paths, inputs, and outputs. Check whether the patch changes behavior outside the reported defect.
- Tests: Look for deleted cases, weaker assertions, broad mocks, skipped tests, and new tests that merely repeat the implementation’s assumptions.
- Dependency and lockfiles: Identify new, removed, or upgraded packages and confirm the changes are intentional.
- Build and package scripts: Inspect commands that run during install, build, test, or release. Look for new shell execution, network access, or changes to the artifacts produced.
- CI, infrastructure, and container files: Check whether a workflow, Dockerfile, IaC file, or deployment setting changes permissions, secrets exposure, network access, or the code path that runs.
- Generated files and formatting: Confirm generated output is expected and that large mechanical changes do not conceal a behavior change or unrelated refactor.
OWASP’s secure-AI guidance emphasizes explicit review of every file and attention to out-of-scope edits. GitHub also warns that AI may suggest fabricated or insecure dependencies. These cautions apply even when the visible code change is small: a package script or workflow can alter the behavior of the repository without changing the application logic.
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Use a scope check before reading for correctness
First compare the changed-file list with the stated fix. For each file, ask what role it plays in the solution and whether that role is necessary. Investigate changes that do not have a clear connection to the defect; do not let a plausible explanation in a pull-request description substitute for inspecting the file.
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What tests should run on an AI-written patch?
Run the project’s relevant checks, then add or select tests that independently verify the intended behavior. The right mix depends on the change: unit and integration tests may be central for application logic, while fuzzing, static analysis, or dynamic checks may be appropriate for parsers, security-sensitive paths, or other high-risk code.
Test the original failure and the boundaries around it
- Keep a regression test for the original defect. It should fail against the affected behavior and pass when the fix works, rather than merely asserting an implementation detail.
- Check ordinary valid cases. Verify the expected behavior for representative inputs and the normal execution path.
- Add negative and adversarial cases. Consider malformed or unexpected inputs, boundary values, authorization failures, and attempts to bypass the intended control.
- Check interactions where relevant. For code that coordinates shared state or parallel operations, test concurrency and failure behavior, not only a single successful call.
- Run the appropriate project gates. Use the relevant unit, integration, fuzz, static, and dynamic checks for the affected code and risk.
Microsoft recommends independent, hand-written security tests and regression tests for the original attack. The key word is independent: tests should measure the required behavior rather than take the generated implementation as their oracle. A test authored by the same generation process can still be useful, but it should not be the only evidence that the patch is correct.
Ask whether a green test result means what it appears to mean
Green CI is evidence only if the checks remain relevant and capable of detecting the defect. Inspect whether tests were removed, assertions weakened, mocks substituted for the real dependency, or coverage narrowed. A test that only verifies that a new helper returns the value the implementation itself constructed may pass while the user-visible requirement remains broken.
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Google describes continuous presubmit as part of its change process, but presubmit does not eliminate the need to assess the tests themselves. Confirm which checks actually ran, whether they cover the changed path, and whether any were skipped or failed without blocking the result. If the patch changes test configuration or the CI workflow, review that change as part of the code change.
How should you check security, dependencies, and execution paths?
Use security checks suited to the code and repository, and examine what new code can execute or access. For AI-generated pull requests, OWASP AISVS recommends relevant SAST, IAST, DAST, secret scanning, infrastructure-as-code scanning, and software-composition analysis. The applicable gates depend on the project, but unresolved critical findings should block a merge rather than be waved through because other checks pass.
Match security checks to the change
Static analysis can flag risky code patterns without running the application; dynamic checks exercise behavior at runtime; software-composition analysis helps surface dependency risks; and secret or infrastructure scans target other parts of the delivery path. No single scan establishes that the fix is secure. Check results in context, follow up on relevant findings, and use elevated review for security-critical files.
Reviewers should pay particular attention to changes involving authentication, authorization, sensitive data, network boundaries, secrets, or deployment permissions. If the patch touches a security-critical path, require a reviewer qualified to assess that path and apply the project’s stronger review requirements. OWASP AISVS says AI-generated code should go through review by a qualified human engineer; automated tools do not replace that judgment.
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Verify every added package and its use
- Confirm that each added package exists in the official registry, rather than trusting a name suggested by generated code.
- Check that it is supported and compatible with project policy, including the project’s rules for pinning versions.
- Review the lockfile and dependency tree to see what is actually being installed, including transitive packages where the available tooling exposes them.
- Read how the code uses the dependency. A real package can still be used insecurely or be unnecessary for the fix.
Do not infer that a package is trustworthy solely because its name looks familiar or because CI downloaded it successfully. Verify its identity and version through the registry and follow the repository’s support and dependency policies.
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Inspect what runs during install, build, test, and deploy
Trace changed scripts and workflows through the lifecycle in which they execute. Flag unexpected shell commands, new network access, altered permissions, or access to secrets. A change that appears to add a library or test can also modify installation or deployment behavior; inspect that path rather than limiting review to runtime source code.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you choose a review workflow for an AI-generated change?
A sound workflow gives reviewers evidence at several independent points: the defect is understood, the entire diff is visible, tests check the requirement, security and supply-chain risks are examined, and a qualified person owns the approval. The appropriate intensity should reflect the code’s risk, not simply the fact that a patch is short.
| Review dimension | What a strong workflow checks | Failure signal |
|---|---|---|
| Defect and acceptance criteria | The original problem and expected behavior are stated; reproduction is available when feasible. | The patch is judged by whether it looks reasonable rather than whether it meets a defined requirement. |
| Diff and scope | Every changed file is reviewed, including tests, dependencies, build files, and workflows. | Review is limited to the main source file or overlooks unexplained edits. |
| Test independence | Regression and negative cases measure behavior independently of the generated implementation. | Tests were weakened, deleted, bypassed with mocks, or only encode the proposed implementation. |
| Security coverage | Relevant automated checks run, findings are handled, and sensitive changes receive elevated review. | A critical finding remains unresolved or a security-sensitive path has no qualified reviewer. |
| Dependency and build-path scrutiny | Packages, versions, scripts, workflows, and execution paths are verified against project policy. | A dependency or executable change is accepted without confirming what it is or what it runs. |
| Reviewer separation and escalation | A qualified human reviewer independent of the generator assesses risk; unresolved issues have a clear escalation path. | The generator’s explanation or self-review is treated as approval. |
| Provenance and auditability | The record identifies how AI was used and leaves accepting humans accountable for the decision. | There is no usable record of the change’s origin, review, or unresolved uncertainty. |
This comparison is a decision aid, not a universal checklist with one fixed severity threshold. Apply the repository’s own policies for required reviewers, blocking findings, and supported dependencies. If the workflow cannot establish that a critical risk is resolved, stop and escalate instead of treating a passing pipeline as permission to merge.
Does an AI-generated fix need a second reviewer?
Not every repository or change has the same review rule, but security-sensitive changes warrant qualified human review independent of the generator, and sensitive files may require two-person or security-team approval. The reviewer should be able to understand the implementation and its risk without relying on the AI’s explanation.
For ordinary changes, follow the project’s review requirements and assess risk in context. For changes to security-critical files or sensitive execution paths, use the stronger gate required by repository policy; OWASP AISVS specifically recommends elevated review for security-critical files. Chromium’s policy says authors must self-review and understand all code, with or without AI tooling, before sending it for review. Self-review is important, but it does not replace the independent approval required by the project’s rules.
What should block a merge, and what happens after approval?
Do not merge while a critical finding or material uncertainty remains unresolved. Escalate when the reviewer cannot explain the fix, the security implications are unclear, or the tests do not establish the expected behavior. Record what is known, what remains uncertain, and who is accountable for accepting the residual risk.
Use a practical decision gate
- Ready for approval: The defect and acceptance criteria are clear, the full diff is explained, relevant tests and security checks have been assessed, and required human reviewers approve.
- Needs changes: The behavior is not independently verified, a file or dependency is unexplained, test integrity is in doubt, or a finding needs remediation.
- Escalate or block: A critical issue is unresolved, a sensitive path lacks qualified review, or the reviewer cannot establish whether the change respects a security or data-handling boundary.
Approval applies to the reviewed patch, not to later edits in the abstract. Google and Chromium guidance support repeating review and presubmit after material changes. If the patch changes substantially after approval, inspect the new diff and rerun the relevant checks before merging. A small edit may still be material if it affects behavior, permissions, dependencies, or the test evidence on which approval relied.
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Record how AI was used, which parts required additional scrutiny, and any uncertainty the author or reviewer could not fully resolve. GitHub’s guidance says to review suggestions before accepting them; the practical implication for a pull request is that a reviewer should be able to trace the proposed change to a human-understood requirement and approval. The accepting humans remain accountable for the merge.
GitHub reports that its custom evaluation harness contains over 2,300 alerts from diverse public repositories with test coverage. That is a description of the evaluation harness, not a general pass rate, vulnerability rate, or guarantee about an individual patch. Do not use it as a substitute for checking the code and evidence in the pull request in front of you.
Frequently Asked Questions
Can I use an AI tool to review the patch that another AI generated?
A second automated review can help surface questions, but it does not satisfy the need for qualified human review on security-sensitive code. Treat any automated critique as a lead to verify against the actual diff, tests, and repository policy.
How much AI use should I disclose in a pull request?
Record enough to make the change’s provenance and review understandable, including how AI contributed and which areas needed human verification. Follow any additional disclosure rules your repository or organization sets.
What if a scanner reports a critical issue that looks like a false positive?
Do not silently ignore or suppress it to get a green result. Have an appropriate reviewer assess the finding, document the rationale and disposition under project policy, and block or escalate until the critical risk is resolved or formally handled.
Do small AI-generated changes need the same checks as large ones?
Review effort should reflect risk and execution path, not line count alone. A one-line change to authorization, a dependency, or a deployment workflow can deserve more scrutiny than a larger low-risk refactor.
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