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You do not need to understand every line of AI-generated code to test it—but you do need to know what the change is supposed to do. Turn the request into observable pass-or-fail conditions, test those conditions independently, run the project’s existing checks, and add security and dependency checks suited to the change. If you cannot explain what a test proves, or the change is consequential or security-sensitive, get a qualified human review before approving it.
Start with the expected behavior, not the implementation
Write the change’s contract in plain language before deciding whether it works. Use the feature request, acceptance criteria, project documentation, and current product behavior to establish what should happen. GitHub’s guidance on [reviewing AI-generated code](https://docs.github.com/en/copilot/tutorials/review-ai-generated-code) recommends checking the result against its purpose, requirements, architecture, and project conventions.
Make the contract concrete by listing the inputs, expected outputs, visible outcomes, constraints, and failure behavior. For example, instead of “the import should work,” specify which file types are accepted, what happens when a file is malformed, and what the user sees after a successful import. A requirement that cannot be stated clearly is not yet a useful test oracle; ask for clarification or narrow the change before approving it.
Build tests from the contract
Choose tests that check outcomes without simply copying the implementation’s assumptions. A useful set usually covers ordinary use, boundaries, invalid or malformed inputs, and relevant historical regressions. For a user-facing flow, an end-to-end test can verify that a user completes the intended task. NIST’s [NISTIR 8397](https://www.nist.gov/publications/guidelines-minimum-standards-developer-verification-software) describes black-box, structural, and historical test cases, as well as fuzzing, among broadly applicable verification techniques.
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- Ordinary cases: Does the feature succeed with valid, typical inputs?
- Boundary cases: What happens at limits such as empty values, maximum sizes, or dates at a cutoff?
- Invalid cases: Are malformed or unsupported inputs rejected safely and clearly?
- Regression cases: Do important behaviors that worked before still work?
When you do not understand the code, keep the test’s assertion tied to a user-visible result or an explicit requirement. A passing test means its assertion held for the cases it exercised; it does not show that the assertion itself is correct or that every important case was covered.
Run the project’s checks and inspect test changes
Build or compile the project where applicable, then run its existing test suite and other normal checks. Also inspect the changes made to tests. AI-generated code may modify, skip, or delete tests in ways that make a suite appear green without preserving its original coverage. GitHub flags deleted or skipped tests as a pitfall; the [OWASP Secure Coding with AI Cheat Sheet](https://cheatsheetseries.owasp.org/cheatsheets/Secure_Coding_with_AI_Cheat_Sheet.html) recommends CI rules that flag test deletions or reduced assertions, with human-reviewed justification for test changes.
Rank #2
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- Check whether tests were deleted, disabled, skipped, or weakened.
- For changed assertions, verify that the new expectation still enforces the requirement.
- Do not treat a successful run as meaningful if relevant tests no longer run.
Choose checks for the risks they can detect
Functional tests are only one part of verification. Static analysis can reveal code patterns that warrant attention; supported secret scanning can catch exposed credentials; and dependency checks can help identify packages that are nonexistent, poorly maintained, or known to have vulnerabilities. NISTIR 8397 recommends measures including static scanning, heuristic secret detection, web application scanning where relevant, and attention to included libraries, packages, and services. OWASP also highlights dependency auditing when AI-generated code may introduce hallucinated or unsuitable dependencies.
| Check | Can help expose | What it needs | What it does not establish alone |
|---|---|---|---|
| Behavioral and end-to-end tests | Incorrect outcomes, edge cases, and regressions in tested flows | A clear contract and representative test inputs | That untested cases work or the test expectations are sound |
| Static analysis and security scanning | Suspicious code patterns and some security weaknesses | Source code and suitable configured tools | That the program behaves correctly in every runtime situation |
| Secret scanning | Potentially exposed credentials or secrets | Changed files or repository content and a supported scanner | That every secret or exposure path has been found |
| Dependency review and audit | Unfamiliar, vulnerable, or unsuitable packages | The package inventory and information about package provenance, maintenance, license, and advisories | That the application uses dependencies safely in all contexts |
Test security behavior separately
For changes involving authentication, authorization, tokens, untrusted input, or sensitive data, write negative and adversarial tests that challenge assumptions. Depending on the feature, check expired tokens, malformed payloads, invalid inputs, boundary conditions, concurrency, and deserialization. OWASP recommends adversarial and negative tests not generated by the AI, manual testing of security-critical behavior, and independent analysis. Its [AISVS Appendix C](https://github.com/OWASP/AISVS/blob/main/1.0/en/0x92-Appendix-C_AI_for_Code_Generation.md) calls for elevated review of security-sensitive files and fuzz or property-based testing for critical behavior.
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These checks complement ordinary feature tests; they do not replace a qualified security review when the impact or uncertainty warrants one.
Use AI to find gaps, not to certify its own work
You can ask an AI assistant to suggest missing cases, explain assumptions, or draft tests from a written specification. Then check every proposal against the contract and add cases the assistant missed. Tests written alongside generated code can share the same mistaken assumptions, so they should not be your only evidence.
Rank #4
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NIST’s [GenAI Code Pilot](https://ai-challenges.nist.gov/code) evaluates tests generated from textual specifications, including an example that asks for edge cases and type-error cases. It supports specification-grounded evaluation; it does not establish that generated tests are automatically sufficient or independent.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Know when not to approve yet
Ask a qualified teammate to review work that is complex, consequential, or security-sensitive. GitHub recommends collaborative review for complex or sensitive changes, and OWASP AISVS calls for qualified human review of AI-generated code. If you cannot state what the change should do or what a test demonstrates, seek clarification, reduce the scope, or hold approval until someone qualified can assess it.
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