Verify AI-generated code the way you would any code whose assumptions you have not yet confirmed: define the expected behavior, inspect the diff, test the change, run the project’s linting and security checks, review dependencies, and have a qualified person assess the result. Passing tests and clean scanner output are useful evidence, not proof that the code is correct or safe.
What verification can—and cannot—tell you
AI-generated code can contain bugs, insecure patterns, outdated APIs, or assumptions that do not fit your project. GitHub’s guidance recommends automated tests and static analysis as initial checks, while OWASP calls for broader security checks and qualified human review for AI-generated code in pull requests. Neither a green test suite nor a clean scan establishes that the implementation meets the requirement in every context.
Use checks as complementary signals: tests exercise selected behavior; linters, type checkers, and static analyzers flag patterns they are designed to detect; dependency checks surface supply-chain concerns. A reviewer still needs to judge requirements, architecture, business logic, and whether findings have been handled appropriately.
How to verify AI-generated code step by step
1. Define the change contract
Before judging the implementation, write down what it must do, which edge cases matter, what security assumptions apply, and what compatibility constraints it must preserve. Compare the requested change with project documentation and established patterns. Ask what the implementation assumes and whether those assumptions are valid. GitHub recommends checking generated code against the surrounding context and intent.
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2. Inspect the diff before running it
Read both the changed code and its tests before compiling or executing generated output. Look for unrelated edits, surprising deletions, hardcoded secrets, unsafe input handling, APIs that may not exist in your project, and dependency or lockfile changes. A test or requirement removed to make a failure disappear is a warning sign, not a successful fix. GitHub specifically flags deleted or skipped tests as a concern in AI-assisted code review.
3. Run focused functional checks
Start with checks that directly exercise the change: compile or type-check where applicable, run targeted unit and integration tests, and test relevant end-to-end user flows. Add cases for missing behavior and important edge conditions; do not rely only on tests generated alongside the implementation. Then run the broader project suite through the same CI process used for other changes, and investigate new warnings and errors.
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When a test fails, determine whether it exposes a defect, a misunderstood requirement, or an outdated test. Do not weaken, delete, or skip a test merely to obtain a green run. GitHub’s guidance recommends automated tests and suggests including test cases in prompts, but the implementation still needs independent verification.
4. Run the repository’s configured code checks
Run the formatter, linter, type checker, and static analyzer the project actually uses. Review warnings in context and address relevant findings; do not assume every warning is a defect or dismiss findings without explanation. GitHub recommends CodeQL or similar scanning as an example, not as a universal tool choice for every language or repository.
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Static analysis can identify certain classes of suspicious code without executing every possible path. It cannot prove that requirements were interpreted correctly or that every defect has been found. A clean result is one layer of evidence, not a safety certificate.
5. Add security checks in proportion to risk
For changes with security implications, use checks that fit your stack and threat surface. OWASP’s AI-assisted secure-coding controls list static application security testing (SAST), interactive application security testing (IAST), dynamic application security testing (DAST), secret scanning, infrastructure-as-code scanning, and software composition analysis on every pull request containing AI-generated code. That checklist is a security-control recommendation; it does not mean every small project has identical infrastructure or access to every scanner.
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Prioritize relevant checks for the change. For example, an edit that adds a package warrants dependency and license review; a change to infrastructure configuration warrants infrastructure-as-code scanning where available. Record any check that could not be run and why, rather than implying it passed.
6. Check dependencies and licenses
For every introduced package, confirm that it exists, comes from the intended publisher, is maintained enough for your needs, and has a license compatible with your project. Inspect both direct dependencies and transitive changes in the lockfile. AI can suggest nonexistent or suspicious packages, so a plausible package name is not sufficient evidence that a dependency is legitimate.
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7. Get qualified review for consequential changes
Ask a qualified human engineer to review security-sensitive, multi-service, or difficult-to-test changes. The reviewer should assess whether the code fits the architecture, correctly implements the business logic, respects project constraints, and responds appropriately to test and scanner findings. OWASP explicitly calls for review by a qualified human engineer. AI-assisted review can help surface issues, but GitHub notes its suggestions can be incomplete or suboptimal and also need review.
8. Keep a record of what was checked
For an auditable handoff, note which tests, lint rules, scanners, and review steps ran, their results, and any exceptions accepted. This makes it easier for another reviewer to understand what evidence supports the change and what remains unverified.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choosing checks for a repository
There is no universally best scanner or fixed score for verifying AI-generated code. Compare a verification setup by whether it covers the behavior and edge cases that matter, which defect classes it can detect, whether it supports your languages and frameworks, and whether it integrates repeatably with CI. Also consider dependency and secret coverage, the false-positive review burden, and whether qualified people can interpret and act on findings.
GitHub’s AI-generated code review guidance recommends automated tests and static analysis and discusses reviewing changes in context. OWASP’s AI and Machine Learning Security Cheat Sheet describes security controls for AI-assisted coding, including scanning and human review. These sources support a layered workflow; they do not rank vendors or establish that any particular tool can guarantee a safe result.
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Before approving or deploying
- The change has a clear expected behavior and its assumptions have been checked.
- The diff contains no unexplained deletions, unrelated changes, or suspicious hardcoded values.
- Relevant tests exercise the intended behavior and important edge cases; failures were investigated rather than hidden.
- Configured formatting, linting, type-checking, and static-analysis checks have been run, with meaningful findings addressed.
- Security checks fit the change’s risk, and any unavailable checks or accepted exceptions are documented.
- New dependencies, their publishers, maintenance status, and license compatibility have been reviewed.
- A qualified reviewer has assessed context and consequential risks, not just the automated results.
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