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Review AI-generated code as a proposed change—not as a finished answer. Before merging, check that it matches the request, passes the project’s relevant tests and analysis, handles security-sensitive behavior appropriately, and fits the codebase well enough to maintain. A green test run is useful evidence, but it does not prove the change is correct or safe.

Start with the requested behavior

Before judging the implementation, restate what the change is supposed to do. Compare the patch with the issue, acceptance criteria, or prompt that authorized it. Identify both the behavior that should change and the behavior or system invariants that must remain unchanged.

  • Which user-visible behavior or system outcome is required?
  • What should stay the same, including error behavior, permissions, data formats, and compatibility?
  • Does the patch introduce behavior outside the request’s scope?

GitHub’s AI-generated code review guidance recommends checking that generated code fits the project’s requirements, architecture, and conventions. Treat that as an intent check, not a judgment based on who or what authored the code.

Read the complete diff before running it

Inspect every changed file, not just the central function. Include tests, configuration, scripts, migrations, dependency manifests, and removed code. Look for changes that expand the scope, silently alter defaults, or remove behavior the request did not ask to remove.

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For each changed file, ask whether the modification is necessary for the requested behavior and whether it creates follow-on work elsewhere. A patch can compile while still carrying avoidable complexity, changing an unrelated interface, or adding configuration that future maintainers must understand.

Run the project’s checks and inspect their output

Use the repository’s established workflow: build or compile, run relevant existing tests, and run configured linting or static analysis. GitHub advises running automated tests and static analysis first. These checks provide evidence about the change; they are not a substitute for reviewing what the checks actually cover.

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  1. Run the project’s documented build or compile command and resolve failures attributable to the patch.
  2. Run the relevant test suite, including focused tests for the changed behavior where available.
  3. Run the configured linter, type checker, or static analysis and examine warnings as well as errors.
  4. Review output for skipped tests, suppressed warnings, or checks that did not run; a successful exit code alone does not establish that every relevant check executed.

Use tools already adopted by the project where possible. GitHub names CodeQL and Dependabot as examples of vulnerability analysis and dependency alerts, and GitHub Code Quality as an example of code quality feedback. Those are examples of tool functions, not a claim that any one product is best for every repository.

Check whether the tests prove the requirement

Compare test assertions with the intended behavior, rather than assuming generated tests are adequate because they pass. A test may repeat the implementation’s assumptions and miss the condition that matters to users or other components.

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For the particular change, consider whether coverage includes the relevant boundaries, invalid inputs, error paths, permissions, data shapes, and integration behavior. Ask: what missing test would expose a plausible regression? Add or request that test when the risk warrants it. The objective is not maximal test count; it is evidence that the requirement and important failure cases are exercised.

Review security-sensitive behavior explicitly

Trace the parts of the patch that handle untrusted input, authentication, authorization, sensitive data, secrets, error messages, or potentially unsafe operations. Check that access controls apply at the correct boundary and that failures do not reveal data or leave the system in an inconsistent state. Run the repository’s available security analysis and investigate findings in the context of the change.

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NIST’s SP 800-218A supplements the Secure Software Development Framework with considerations for AI model development through the software development life cycle. It is framework guidance, not a requirement to adopt a particular tool. The NIST NCCoE’s DevSecOps reference model describes AI-generated outputs as inputs that still go through established peer review, security validation, automated testing, and approval workflows.

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Verify every added or changed dependency

For each new or altered package, verify that the package exists and is the intended one, and check its provenance, maintenance status, and license compatibility with the project. A plausible-looking package name is not proof that it is legitimate or suitable; package-name confusion can create supply-chain risk, including slopsquatting.

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Also review lockfile and manifest changes together. Confirm that version changes are intentional and that the dependency is actually needed for the feature, rather than an avoidable addition that creates future upgrade and security work.

Judge maintainability and fit with the codebase

Review whether the patch uses the project’s established patterns and leaves future changes understandable. GitHub’s review guidance calls attention to readability, maintainability, architecture, conventions, and whether code can be divided into smaller, testable units.

  • Look for duplicate logic, unnecessary abstractions, unclear names, and complexity that does not serve the requirement.
  • Check whether existing project utilities or patterns already solve the problem.
  • Look for tightly coupled code or oversized units that make testing and future changes harder.
  • Prefer the smallest clear change that satisfies the requirement without obscuring important behavior.

Keep human approval in the workflow

Ask a teammate to review complex or sensitive changes, as GitHub recommends. Reviewers should be able to trace the change from requirement to implementation, tests, and approval. Do not allow an AI-generated fix or corrective action to bypass the repository’s normal review gates.

The NIST NCCoE reference model says AI-generated corrective actions should not modify software, configurations, or system state without review and approval through established DevSecOps processes. That principle matters especially when a system can make changes beyond a local draft—for example, changes that affect production configuration or state.

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A practical merge decision

Merge only when the change’s intent is clear, the full diff is within scope, relevant checks have been run and understood, test coverage is credible for the behavior, security and dependency concerns have been addressed, and the code is maintainable within the project. If a critical assumption remains unverified, request a revision or add the evidence needed before approval. AI authorship alone is not evidence of a defect, and passing checks alone is not evidence of safety.

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