Evaluate an AI coding assistant’s suggestion as a proposed code change—not as a verified answer. Read it in the context of the repository, check that it meets the requirement, run appropriate builds and tests, inspect security and dependencies, and have a knowledgeable human approve the result before it ships.
1. Start with the requirement and the complete diff
Before judging whether a suggestion looks reasonable, restate the change it is meant to make. Compare that requirement with the full diff, including surrounding code, configuration, generated tests, and any other files changed. A correct-looking snippet can still solve the wrong problem or conflict with the project’s architecture and conventions. GitHub’s code review guidance emphasizes understanding the intent and repository context rather than assessing code in isolation.
- Trace the change from the requested behavior to the code that implements it.
- Check whether it uses the project’s established patterns for errors, data access, naming, and configuration.
- Look for unrelated edits, omitted files, or tests that merely repeat the implementation’s assumptions.
2. Establish that it works
Run the checks appropriate to the repository: build or compile the project, run relevant tests, and inspect warnings and errors. Passing tests are useful evidence, but they do not prove the change is correct if the tests do not cover the requirement or an important edge case. GitHub recommends functional checks as part of reviewing AI-generated code.
- Run the focused tests for the affected behavior, then the broader test suite where practical.
- Check whether new behavior needs tests that are missing, including failure and boundary cases.
- Confirm that the tests exercise the intended behavior rather than only confirming that the code runs.
When a check fails, determine whether the failure comes from the proposed change, an existing issue, or the test environment before deciding what to do. Do not treat a clean build as a substitute for checking behavior against the requirement.
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3. Review security, dependencies, and commands
Inspect what the change adds or alters at trust boundaries: input handling, permissions, authentication, data access, and error paths. Review new dependencies and proposed commands before using them; code that compiles may still introduce unsafe behavior or unnecessary supply-chain risk. Use the project’s applicable dependency, static-analysis, and security-scanning tools, but treat their results as evidence to investigate—not a replacement for review. GitHub’s responsible-use guidance and OWASP’s Secure Coding with AI Cheat Sheet both support combining testing and security checks with human judgment.
- Check whether inputs are validated and whether access controls match the intended users and operations.
- Examine new packages, scripts, shell commands, and configuration changes before executing or accepting them.
- Run security checks suited to the language and repository, then investigate findings in the context of the change.
4. Challenge the assumptions the code makes
Generated code can be plausible while being syntactically or semantically wrong, incomplete, or misaligned with developer intent. Ask what happens outside the happy path, and compare those answers with the actual product requirements. GitHub’s guidance on using Copilot Chat in GitHub specifically cautions that generated output needs checking for correctness, intent, and security.
- What happens with empty, malformed, oversized, duplicated, or unexpected input?
- How does the code behave when a network call, file operation, or dependent service fails?
- Are data boundaries, permissions, and error handling consistent with the rest of the application?
- Could concurrency, retries, or partial completion leave the system in an invalid state?
These are review prompts, not a universal checklist of defects. Prioritize the cases that matter to the change’s real requirements and operating context.
5. Keep a human accountable for approval
A person who understands the changed code should own the approval and be able to maintain the result. OWASP states in its Secure Coding with AI Cheat Sheet: “AI tools do not accept responsibility for the code they generate.” If the reviewer cannot explain what the change does, why it is safe, and how it fits the project, the suggestion is not ready to ship. Record the approval and any tool or version details when the team’s process requires that audit trail. OWASP’s AI Security Verification Standard includes human-review and automated-security-testing criteria; consult its current edition for version-specific requirements.
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Choose checks by what they can establish
No single review method covers every risk. Compare checks by the evidence they provide and the questions they cannot answer:
| Check | Useful evidence | What it cannot establish alone |
|---|---|---|
| Build or compile | Whether the project can be built or compiled under the check’s conditions. | Whether the implementation satisfies the requirement or handles untested cases. |
| Tests | Whether covered behaviors pass under the test conditions. | Whether important behaviors are missing from test coverage or the tests reflect the right requirements. |
| Static analysis and security scans | Potential issues detectable by the selected tools and rules. | Whether project-specific architecture, intent, and all security risks have been correctly addressed. |
| Human review in repository context | Whether a knowledgeable reviewer finds the change consistent with intent, project patterns, and relevant requirements. | Proof that untested runtime behavior is correct; pair review with appropriate checks. |
This is a way to select complementary checks, not a ranking of products. The cited guidance does not establish a cross-vendor benchmark or a universal production defect or vulnerability rate, so a percentage would not be a sound basis for approving an individual change.
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