Review AI-Generated Code Before Merge

TL;DR

Review AI-generated code like any other untrusted implementation: inspect the diff, rebuild it, test the changed behavior, check security-sensitive paths, and only merge after a reviewer can explain why the code is correct.

The useful mental model is simple: AI can propose code, but the reviewer owns the merge decision. The pull request should contain enough evidence for a human maintainer to verify the change without relying on generated confidence. Even when an AI reviewer provides comments or suggested changes, validate that feedback carefully and supplement it with human review before merging.

Prerequisites & Requirements

• {"task": "Clean checkout", "description": "You can rebuild the branch from scratch and reproduce the test results"}
• {"task": "Project test commands", "description": "You know the focused tests, linters, and type checks for the touched code"}
• {"task": "Security scanning", "description": "Code scanning, secret scanning, dependency review, or local equivalents are available for risky diffs"}
• {"task": "Reviewer authority", "description": "You can request changes when the PR lacks tests, source links, or a clear rollback path"}

Core Concepts Explained

AI output is an implementation, not evidence

Generated code may be useful, but the explanation around it is not proof that the change is correct. Treat claims such as "all edge cases are handled" or "no security impact" as review prompts. Ask for the command, test, trace, design reference, or code path that proves the claim.

Review behavior before style

Generated diffs often look polished. Start with behavior: what input changed, what output changed, which permissions changed, what data moves, and what can fail. Style cleanups can wait until the reviewer understands the actual runtime effect.

Separate generated-size risk from feature risk

A small generated diff can change authorization logic. A large generated diff can be mostly generated fixtures. Review risk by the code path and blast radius, not by whether the author used an AI tool.

Step-by-Step Implementation Guide

1. Freeze the PR scope. Ask the author to state which files were generated, which were manually edited, and what user-visible behavior should change. If the PR mixes unrelated refactors, generated rewrites, and feature work, request a smaller branch before reviewing.

2. Rebuild from a clean checkout. Pull the branch, install dependencies using the repository's documented workflow, and run the focused checks for the touched package. Do not rely only on screenshots, generated summaries, or copied terminal output in the PR body.

3. Review security-sensitive paths first. Start with authentication, authorization, secrets, payments, migrations, data deletion, networking, deserialization, sandbox escapes, release automation, and permission changes. These paths get review priority because a plausible-looking generated patch can still change a trust boundary.

4. Check secrets and dependency changes. Run secret scanning or a local equivalent before merge. If the PR changes package manifests, lockfiles, container images, GitHub Actions, or third-party SDKs, inspect the dependency diff and confirm why each new dependency is required.

5. Read the diff in small slices. Review one behavior path at a time. For each slice, ask: what invariant should stay true, what test proves it, what user data is touched, and what happens when the new code fails?

6. Require focused tests for changed behavior. Unit tests are usually enough for pure functions. Integration tests are better for permission checks, persistence, API clients, migrations, and concurrency. If a test would be too expensive, ask for a documented manual verification command.

7. Verify AI-written comments and docs. Generated comments can drift from the code they describe. Check that public docs, migration notes, and inline comments match the implementation and do not promise unsupported behavior.

8. Review agent-created PRs as production code. If an AI coding agent opened the pull request, inspect the final diff thoroughly before merging. Confirm that the agent's summary matches the files, that requested review comments were actually addressed, and that no unrelated generated changes slipped in.

9. Merge only after the reviewer-owned checklist passes. The reviewer should be able to summarize the change, name the highest-risk file, point to the verification evidence, and explain the rollback plan.

Reviewer Checklist

• {"task": "Scope is narrow", "description": "The PR changes one behavior or one coherent workflow"}
• {"task": "Generated files are identified", "description": "The author says which parts came from an AI coding tool"}
• {"task": "Clean build succeeds", "description": "The branch installs and checks from a fresh checkout"}
• {"task": "Tests cover the changed behavior", "description": "Focused tests or a manual verification command are visible to reviewers"}
• {"task": "Security-sensitive paths are inspected", "description": "Auth, permissions, data movement, secrets, dependencies, and automation changes are reviewed first"}
• {"task": "Scanners are clean or triaged", "description": "Code scanning, secret scanning, and dependency alerts are resolved or explicitly accepted"}
• {"task": "Claims have evidence", "description": "Generated explanations are backed by code, tests, logs, docs, or maintainer reasoning"}
• {"task": "Rollback is understandable", "description": "The team knows how to revert or disable the change if production behavior regresses"}

When to Block the Merge

Block or request changes when the PR:

• Adds a dependency without a reason, version pin, lockfile update, or license/security check.
• Changes authentication, authorization, payment, data deletion, or release automation without focused tests.
• Includes generated code that the author cannot explain.
• Relies on AI-written claims instead of reproducible verification.
• Moves secret handling, logging, or telemetry into a new path without privacy review.
• Makes sweeping style or architecture rewrites while claiming to fix a small bug.

Troubleshooting

• The PR is too large to review: ask for a smaller PR that separates generated refactors from behavior changes.
• CI is green but the risk still feels high: add a targeted test around the risky path before merge.
• The author says the AI tool verified it: ask for the actual command output, test case, or source link a maintainer can inspect.
• A scanner reports a warning the team accepts: document the rationale in the PR so future reviewers can see the decision.

Duplicate Check

This guide is intentionally about the maintainer workflow for reviewing AI-generated pull requests before merge. Adjacent entries in the repository cover code review tools, security scanners, and review-oriented agents, but they do not provide a source-backed guide for the human merge decision on generated code.

References

• GitHub Docs: Reviewing proposed changes in a pull request - https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/reviewing-changes-in-pull-requests/reviewing-proposed-changes-in-a-pull-request
• GitHub Docs: Responsible use of GitHub Copilot code review - https://docs.github.com/en/copilot/responsible-use/code-review
• GitHub Docs: Review output from Copilot - https://docs.github.com/en/copilot/how-tos/copilot-on-github/use-copilot-agents/review-copilot-output
• GitHub Docs: About code scanning - https://docs.github.com/en/code-security/concepts/code-scanning/about-code-scanning
• GitHub Docs: About secret scanning - https://docs.github.com/en/code-security/concepts/secret-security/about-secret-scanning
• GitHub Docs: About dependency review - https://docs.github.com/en/code-security/concepts/supply-chain-security/about-dependency-review
• NIST SP 800-218: Secure Software Development Framework - https://csrc.nist.gov/pubs/sp/800/218/final