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Use the same acceptance bar for AI-generated code as for any other change: verify the intended behavior, run tests and static checks, inspect the implementation and its tests, review dependencies and security-sensitive changes, and require human review before merging. A passing test run is evidence only for the behaviors those tests actually exercise.
Start by defining what the change must do
Compare the proposed code with the issue, specification, or acceptance criteria. Write down the expected behavior and important failure cases before relying on the code’s explanation of itself. Check assumptions about business rules, existing interfaces, and the project’s architecture; generated code can be plausible while solving the wrong problem.
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Run the project’s functional and static checks
Build or compile the project, run the relevant automated tests, and examine warnings as well as errors. Include the project’s static analysis, linting, or other established quality checks in this first pass. Functional tests execute code to check observed behavior; static analysis can flag certain patterns without running the program. Neither covers every possible defect, so use them as complementary evidence.
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Check that new or modified tests actually express the required behavior and include meaningful failure cases. Inspect changes to existing tests for deleted tests, skipped cases, reduced assertions, or other weakening. GitHub’s code-review guidance specifically recommends asking why a failing test was deleted: About pull request reviews.
A green run does not establish behavior that the tests never exercise. If the change touches an edge case or an important contract, verify that the test suite checks it rather than inferring coverage from the overall pass result.
Inspect the implementation and its interfaces
Read the diff in the context of surrounding code. Confirm that APIs and configuration options exist, that constraints in the request were followed, and that error handling and edge cases make sense. Look for unnecessary changes, unclear logic, and departures from established project patterns that would make future maintenance harder.
Review dependencies and security-sensitive changes
For each added or changed package, verify that it exists, comes from an acceptable origin, is maintained, has a license the project can accept, and is actually needed. Treat dependency review as distinct from code review: a correct-looking call site does not establish that the package is trustworthy or suitable.
Run the security and quality checks appropriate to the project, including dependency checks and static security analysis where available. GitHub cites CodeQL and Dependabot as examples and recommends incorporating style, linting, security, quality, and coverage checks into CI: GitHub security features.
Give security-critical changes qualified review
Require review by someone qualified for the risk when generated changes affect authentication, authorization, cryptography, identity and access management (IAM) policy, CI/CD workflows, deployment manifests, or sandbox and network policies. OWASP’s AI Security Verification Standard identifies these as areas warranting particular attention to qualified human review: OWASP AISVS.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make repeatable checks part of the merge gate
Run agreed checks automatically in CI so the same build, test, and analysis steps apply to every pull request. Where your platform supports it, make required checks or quality thresholds conditions of merging rather than relying on reviewers to remember them.
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GitHub Code Quality documents pull-request findings from deterministic CodeQL rules, optional Cobertura coverage metrics, and rulesets that can enforce quality or coverage thresholds. The documentation lists availability for GitHub Team and GitHub Enterprise Cloud; confirm current plan availability and feature details in GitHub’s Code Quality documentation.
Quick Recap
Best Value
Use each check for the question it can answer
| Check | What it helps establish | What it does not establish by itself |
|---|---|---|
| Functional tests | Whether tested scenarios exhibit the expected behavior. | Whether untested behavior, architecture, or assumptions are correct. |
| Static analysis | Whether code matches patterns or rules detectable without execution. | Whether the feature meets its intended behavior in every context. |
| Dependency review | Whether packages are real, maintained, acceptably sourced and licensed, and needed. | Whether the application’s use of a package is correct. |
| Human review | Whether the change fits intent, architecture, maintainability expectations, and risk. | A substitute for running the project’s checks. |
| CI merge gates | Whether agreed, repeatable checks pass before merge. | A guarantee against defects that the checks do not detect. |
A practical pre-merge checklist
- Confirm intent: compare the diff with the issue or acceptance criteria and verify its assumptions.
- Build and test: compile or build, run relevant automated tests, and resolve errors and meaningful warnings.
- Run static checks: use the project’s established analysis, lint, quality, and security checks.
- Audit test changes: ensure tests cover required behavior and were not deleted, skipped, or weakened without justification.
- Review the diff: verify APIs, interfaces, edge cases, readability, and fit with project patterns.
- Check dependencies: validate package existence, provenance, maintenance, license, and necessity.
- Assign the right reviewer: route security-critical changes to a qualified reviewer.
- Enforce the process: make repeatable checks required in CI or the repository’s merge rules where available.
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