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Verify an AI-generated change with several independent kinds of evidence: first compare it with the requirement and existing design, then run the project’s build, tests and analyzers, inspect every changed file, trace affected behavior beyond the diff, and have a qualified human review and approve it. A green test run or scanner result is useful evidence, not proof that the change is correct or secure.
The routine below turns that principle into a repeatable pre-merge gate for pull requests, whether an assistant wrote one function or changed application code, dependencies and CI configuration together.
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What “verified” should mean for AI-generated code
Verification is a chain of evidence tied to the intended behavior. It is not a vote on whether an AI summary sounds plausible. A responsible reviewer should be able to answer four questions:
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- What exactly changed in the repository, including files that were not part of the original request?
- Which tests, static checks and security controls exercised the changed surface, and what did they not cover?
- Can a qualified human explain the risks and own the approval?
GitHub’s guidance for reviewing AI-generated code puts functional checks first, followed by context, code quality, dependency review and AI-specific review. NIST’s developer-verification guidance describes complementary techniques rather than a single decisive test. OWASP guidance adds a practical warning: review the complete change and use independent, adversarial tests instead of trusting an agent to evaluate its own output.
#1 Best Overall
1. Establish intent and a clean baseline
Start before opening the generated diff. Write down what the change is supposed to do and what must remain unchanged. Use the issue, acceptance criteria, design notes, API contracts, threat model, project conventions and current production behavior as the baseline.
Turn the request into observable outcomes
- State the normal success path in terms a test can observe.
- List validation rules, authorization decisions, data formats, latency or availability constraints, and compatibility requirements.
- Identify non-functional obligations such as logging, privacy, auditability, migration safety and rollback behavior.
- Record explicit exclusions. A request to add an endpoint does not automatically authorize changes to authentication, deployment files or unrelated dependencies.
Do not treat the model’s explanation, generated commit message or pull-request summary as evidence of intent. Those are claims to check against the repository and the request.
Capture the pre-change behavior
Run the relevant tests on the parent commit and, when practical, exercise the existing behavior manually or with a small reproducible script. Note warnings, flaky tests and known failures before reviewing the new code. A baseline prevents you from attributing an old failure to the generated change and exposes regressions that a narrow new test might miss.
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Locate the established pattern for similar work: error handling, dependency injection, transaction boundaries, authorization middleware, serialization and configuration. An implementation can satisfy a happy-path example while violating an invariant used elsewhere. Compare neighboring modules and documented design decisions before deciding that a novel structure is acceptable.
2. Run automated checks in an intentional order
Automated checks are fastest when failures are easy to localize. Adapt the commands to the project’s documented toolchain; do not invent a passing command simply because an agent suggested one.
- Build or compile. Use the repository’s normal build command and treat new compiler errors and warnings as review findings. Confirm that generated artifacts are reproducible and that the intended target, profile and feature flags were used.
- Run focused tests. Start with tests closest to the changed module, then run integration and end-to-end tests that cross its boundaries.
- Run the full supported suite. A focused suite speeds iteration; the complete suite checks for regressions in callers and shared behavior.
- Run static analysis. Execute the project’s configured linter, type checker and bug-pattern analyzer. Examples of implementable options include CodeQL and Semgrep Community Edition; use the rules and severity policy already adopted by the project.
- Run applicable security and dependency checks. Check secrets, software composition, infrastructure-as-code and web-application behavior when the changed surface warrants them.
- Record results and environment. Save the commit identifier, tool versions, configuration, warnings and any skipped checks. “Passed” is meaningful only when the reviewer knows what actually ran.
Investigate warnings instead of hiding them
Generated code often compiles with deprecations, unreachable branches, implicit type conversions or disabled rules. Do not lower a rule’s severity, add a blanket suppression or rewrite a test merely to obtain a green pipeline. If a finding is a false positive, document the reason and scope the suppression to the smallest expression or file.
Match checks to the changed technology
A library change may need API-compatibility and dependency checks; a database migration needs upgrade and rollback testing; a parser needs malformed-input and resource-exhaustion cases; a deployment edit needs a dry run and policy validation. NIST lists threat modeling, automated and structural tests, historical regression tests, fuzzing, secret checks, dependency checks and web-application scanning as complementary techniques. That is a menu for risk-based selection, not a requirement to run every scanner on every one-line edit.
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3. Inspect the complete diff, not just the obvious function
Review the diff from the parent commit to the proposed tip, then inspect each changed file in context. Generated changes frequently include incidental edits that are easy to miss when a pull request description highlights only one function.
| Changed area | Questions to answer |
|---|---|
| Application code | Are control flow, error handling, resource ownership and input validation consistent with neighboring code? |
| Tests | Were tests deleted, assertions weakened, fixtures broadened or mocks substituted for real behavior? |
| Lockfiles and manifests | Did a new or upgraded package appear, and is its source, license, maintenance status and transitive impact acceptable? |
| CI/CD workflows | Were permissions, runners, artifacts, deployment targets or required checks changed? |
| Configuration and infrastructure | Did defaults, network exposure, secrets handling, access policies or resource limits change? |
| Formatting and generated files | Is the volume of noise hiding a semantic edit, and are generated outputs reproducible? |
| Agent instruction or rule files | Did the change alter prompts, repository rules, hooks or policies that govern later automated edits? |
Use version-control commands that expose both names and content, for example a name-status view followed by a complete patch and a comparison with the merge base. The exact command varies by hosting workflow; the essential property is that renamed, deleted, binary, hidden and configuration files are included. Never approve from a generated summary alone.
Check scope and provenance
For every hunk, ask why it exists and which requirement authorizes it. Revert unrelated formatting churn, opportunistic refactors and permission changes unless they are explicitly reviewed. Preserve a record of the human requester, the generated change and any subsequent manual edits so the approval covers the actual artifact being merged.
4. Trace impact beyond changed lines
A diff is a map of edits, not a model of runtime behavior. Follow callers and callees, data flow and shared invariants into code that did not change.
Build a small impact map
- List public entry points touched directly or indirectly: routes, commands, jobs, event handlers and exported functions.
- Trace inputs from their origin through parsing, validation, authorization, transformation, storage and output.
- Identify shared state, caches, transactions, queues and retries. Check whether the new path preserves ordering, idempotency and cleanup.
- Find existing checks that enforce invariants, then verify the generated path reaches them rather than duplicating or bypassing them.
- Inspect callers for assumptions about nullability, error types, timing, status codes, schema versions and side effects.
Review depth should rise with consequence. Authentication and authorization, cryptography, deserialization, file uploads, parsers, dependency changes, deployment controls and pipeline permissions deserve a wider call graph and more negative tests than a local display-only change. OWASP’s secure-code-review guidance specifically warns that focusing only on the changed lines can miss an invariant broken elsewhere.
5. Challenge the tests instead of counting them
Tests provide evidence only for the behavior they exercise. A large passing suite can still encode the wrong requirement, omit an important branch or be weakened by the generated patch.
Look for test manipulation
- Deleted or renamed tests, especially those covering failure paths.
- Assertions replaced with snapshots, truthiness checks or “does not throw” statements that no longer verify the outcome.
- Mocks that replace the real parser, authorization layer, database constraint or network boundary being changed.
- Fixtures altered to match the implementation rather than the documented contract.
- Coverage exclusions, disabled lint rules or timeouts increased without an explanation.
Add cases the generator did not author
Write independent tests for invalid, missing, oversized and unexpected inputs; boundary values such as zero, empty collections, maximum lengths and time transitions; duplicate requests and retries; permission differences; partial failures; concurrent access; and malformed serialized data. For security-sensitive code, include abuse cases designed by a reviewer who did not rely on the same agent’s assumptions. OWASP recommends adversarial or negative tests that are independent of the generator and cautions that a passing suite is not security assurance.
Use multiple testing perspectives
| Technique | Useful evidence | Typical blind spot |
|---|---|---|
| Functional or black-box tests | Externally visible behavior against requirements. | Unexercised branches, hidden state and interactions. |
| Structural tests | Paths, branches, conditions and error handling inside the implementation. | They can provide high coverage of an incorrect design. |
| Historical regression tests | Protection against previously fixed defects returning. | They cover known history, not new failure modes. |
| Fuzz testing | Unexpected inputs, parser edge cases and robustness under variation. | It may miss business rules and meaningful multi-step workflows. |
| Manual exploratory tests | Workflow context, usability and business-logic surprises. | Limited repeatability unless the scenario is captured. |
6. Apply security and dependency checks to the actual risk
Security verification should follow the changed attack surface. For a pull request that handles credentials, permissions, untrusted serialization or deployment, make security controls merge-blocking according to policy. For a documentation-only edit, running every dynamic scanner may add noise without useful evidence.
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- Dependencies: review direct and transitive additions, lockfile changes, known advisories, integrity metadata, license obligations and whether the package is actually necessary.
- Static application security testing: run configured rules for injection, access control, unsafe deserialization, path traversal and other detectable patterns.
- Dynamic or interactive checks: use web-application scanning or runtime instrumentation when the change affects a deployed endpoint and the environment is suitable.
- Infrastructure and pipeline: validate identity permissions, artifact provenance, protected branches, deployment approvals and infrastructure policy when workflows or infrastructure files changed.
- Fuzzing and abuse testing: prioritize parsers, protocol handlers, file uploads, compilers and other inputs where malformed data can cause crashes or resource exhaustion.
OWASP’s AI Security Verification Standard 1.0 identifies SAST, IAST, DAST, secret scanning, infrastructure-as-code scanning and software-composition analysis as controls that may apply to AI-generated-code pull requests. The control also gives CVSS 9.0 or higher as an example critical-finding threshold for blocking a pull request, while allowing an organization’s equivalent policy threshold. Treat that number as a policy example, not a universal substitute for judgment.
7. Make human accountability explicit
The approving engineer must understand the change well enough to explain its behavior, limitations and residual risk. OWASP AISVS control AC.4.1 requires review by a qualified human who is separate from the identity that requested generation; the AI agent does not count as the reviewer. Separation of duties is especially important when the person who prompted the change cannot independently verify its security or business impact.
“Manual Code Review complements automated security testing tools (SAST/DAST) by focusing on areas where human analysis provides the most value, including business logic validation, complex security implementations, and context-specific vulnerabilities.” — OWASP Secure Code Review Cheat Sheet
Raise the approval bar for security-critical files: require a domain owner, a second reviewer, threat-model updates or an explicit risk acceptance. Block the merge for critical findings under your organization’s policy, and record accepted lower-severity findings with an owner and due date. Approval is an accountability decision, not a statement that every possible defect has been eliminated.
A practical pre-merge routine
The following sequence works as a pull-request checklist and can be automated where evidence is objective.
- Read the requirement. Write success, failure and non-functional criteria; note files and systems that should not change.
- Establish the baseline. Run the parent commit’s relevant checks and record existing failures, warnings and environment details.
- Inspect repository status. Confirm the proposed branch contains only intended commits and that ignored, generated or untracked files are not hiding changes.
- Review the full diff. Examine source, tests, manifests, lockfiles, workflows, configuration, generated outputs and instruction files.
- Run build and tests. Compile, execute focused tests, then the supported full suite; investigate every new warning or failure.
- Run analyzers. Execute linting, type checks, configured static analysis, secret checks and dependency checks appropriate to the changed surface.
- Trace impact. Follow callers, data flow, authorization boundaries, persistence and concurrency. Compare the change with established patterns.
- Attack the assumptions. Add independent negative, boundary, malformed-input and concurrency cases. Verify that tests assert intended outcomes rather than implementation details.
- Review security consequences. Check permissions, cryptography, deserialization, uploads, secrets, dependencies, infrastructure and deployment behavior where relevant.
- Obtain accountable approval. A qualified human, separate from the generation identity, explains the evidence, remaining uncertainty and any accepted risk before merging.
Common failure modes and recovery
“All tests passed, so merge it.”
First inspect which tests ran, whether assertions were weakened and which requirements lack coverage. Add independent cases for omitted behavior and run integration paths that cross the changed boundary.
“The scanner is green, so it is secure.”
Check scanner scope, rule configuration, ignored files and data-flow limits. Perform manual business-logic and authorization review; a clean result cannot establish correctness or reveal every context-specific vulnerability.
“The summary says only one function changed.”
Compare the merge base with the tip and review every path, including lockfiles, workflows, tests and agent rules. Revert or separately review out-of-scope edits.
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Read the assertions and fixtures. Coverage can rise while tests merely mirror generated branches or accept any output. Require assertions tied to the requirement, including rejection and boundary behavior.
Best Value
“A dependency update is harmless.”
Inspect the lockfile and transitive graph, advisory status, integrity data, license impact and runtime permissions. Test startup, deployment and rollback, not just compilation.
“The AI can review its own patch.”
Use an agent for explanations or candidate tests if policy permits, but keep approval with a qualified human who did not rely on the same identity to generate and validate the change.
Turning the routine into repository policy
Store the checklist with the project’s contribution rules and make objective gates required in the pull-request workflow. Require build and test status, static-analysis results, dependency and secret checks where applicable, and an explicit reviewer sign-off for sensitive paths. Keep logs of tool versions and configurations so a future reviewer can reproduce what “passed” meant.
Define ownership by file or subsystem, specify which findings block merges, and document the exception process. A narrowly scoped, time-bound exception is safer than a permanent suppression. Review the policy as the codebase, threat model and AI tooling change; NIST’s 2021 guidance page was updated on October 6, 2026, illustrating why verification practices need maintenance rather than a one-time setup.
The decision to merge
Merge only when the implementation matches the stated intent, the complete diff is in scope, relevant checks have run with new findings explained, independent tests challenge the risky assumptions, and an accountable human can describe what remains uncertain. The result is not a claim of perfection. It is a documented, proportionate decision that the available evidence and review depth are adequate for the change’s consequences.
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