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Verify an AI-generated change as if it were an untrusted contribution: first prove it matches the requirement, then run the project’s build, tests, static analysis, and security checks, and finally inspect the complete diff and affected data flows. A passing test suite or clean scanner is evidence for only the cases and patterns those tools cover; a qualified human must understand and approve the change before merge.
The routine below turns that principle into a repeatable pre-merge gate, with deeper review for authentication, authorization, cryptography, parsing, deserialization, uploads, dependencies, deployment, and CI/CD changes.
What “verified” means for AI-generated code
Verification is a chain of evidence, not a single green check. You are answering four separate questions:
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- Behavior: Does it work for expected inputs and fail safely for invalid, boundary, and adversarial inputs?
- Change surface: What did the generator actually modify, including files that were not mentioned in its summary?
- Risk and ownership: What security, reliability, privacy, operational, or compliance consequences remain, and can a human reviewer explain and own the approval?
GitHub’s guidance puts functional checks first, followed by context, code quality, dependency, and AI-specific review. NIST’s developer-verification guidance treats static analysis as one technique among several, not as a substitute for testing or reasoning. OWASP guidance adds full-diff inspection, adversarial testing, and separation of duties.
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Step 1: Establish intent and a baseline before running tools
Translate the request into observable behavior
Read the issue, acceptance criteria, design notes, API contracts, and relevant documentation before looking at the generated explanation. Write down what must be true after the change, what must remain unchanged, and which failures are acceptable. A model-generated summary describes what the agent believes it did; it is not evidence that the request was met.
For a feature, identify inputs, outputs, side effects, authorization rules, persistence changes, and error behavior. For a bug fix, capture the original failure, the expected corrected result, and regressions that the fix could introduce. For a refactor, define the invariants that must remain identical. This baseline gives you an independent oracle against which to judge both the code and its tests.
Check architecture and project conventions
Locate the modules that own the behavior, the established design pattern, and the existing validation or error-handling path. An implementation can pass local tests while violating boundaries that the project relies on, such as bypassing a service layer, writing directly to a data store, or duplicating authorization logic. Compare naming, dependency direction, configuration practices, logging, and compatibility policy with neighboring code.
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Record a clean starting point
Confirm which branch and commit you are reviewing, whether the working tree contains unrelated edits, and which checks pass before applying the generated change. A baseline prevents you from attributing pre-existing failures to the AI contribution and makes later failures easier to localize. If the repository already has failing checks, record them explicitly and do not silently treat a new green result as proof that the change is safe.
Step 2: Run project checks in a useful order
Build or compile first
Use the project’s standard build or compile command. Compilation catches type, interface, generated-code, and packaging errors before you spend time interpreting higher-level failures. Treat new warnings as review items, not harmless noise; an AI change may introduce an implicit conversion, deprecated API, unreachable branch, or unsafe configuration that does not fail the build.
Run focused tests, then the full suite
Start with tests closest to the changed behavior for fast feedback, then run the complete automated suite required by the repository. Include unit, integration, contract, end-to-end, and migration tests when the change crosses those boundaries. Capture the exact command, environment, and result so another reviewer can reproduce it.
Inspect failures rather than rerunning until they disappear. A flaky test, a skipped test, or an environment-dependent pass is not equivalent to a deterministic pass. Check whether the generated patch changed test discovery, fixtures, timeouts, snapshots, or test configuration.
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Run static analysis and quality checks
Execute the analyzers configured by the project, such as CodeQL, Semgrep Community Edition, or Snyk Code where they are already approved and integrated. Also run language linters, formatters, type checkers, and compiler warnings. Static analysis can identify detectable bug patterns and coding-standard violations, but a clean result cannot prove that business logic, authorization intent, or untested requirements are correct.
Match security and dependency checks to the surface
For a change that touches dependencies, run software-composition analysis and review lockfile changes. For secrets or configuration, run secret checks and inspect generated files and environment handling. Infrastructure or deployment edits call for infrastructure-as-code scanning and deployment-policy checks. Web-facing changes may require dynamic or web-application scanning. NIST’s list also includes threat modeling, fuzzing, black-box and structural testing, historical regression tests, and checks on included software; apply the techniques that fit the changed surface rather than mechanically running every tool on every tiny edit.
Step 3: Inspect the complete diff, not the agent’s summary
Make the review set explicit
Start with a file list, then open the complete patch. Useful Git commands include:
git status --short
git diff --stat
git diff --name-status
git diff --check
git diff -- .These commands reveal uncommitted files, the size and names of changes, whitespace errors, and the actual line-level patch. Review the final tree as well as the diff when generated files, renames, or ignored files could hide an effect.
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| File or change category | Questions to answer |
|---|---|
| Production code | Does control flow, error handling, data validation, and resource management match the requirement? |
| Tests and fixtures | Were tests deleted, weakened, skipped, or changed to assert the generated implementation rather than intended behavior? |
| Lockfiles and dependency manifests | Did versions, sources, transitive packages, licenses, or integrity data change as a side effect? |
| CI/CD workflows | Were permissions, triggers, artifact handling, deployment gates, or secret exposure changed? |
| Configuration and infrastructure | Did defaults, network exposure, identity bindings, storage, or runtime limits change? |
| Agent instruction or rule files | Did the generator alter files that influence future automated edits or review behavior? |
| Formatting and generated files | Is the churn necessary, and could it conceal a functional change? |
OWASP warns that reviewers who focus only on the obvious task files can miss consequential out-of-scope edits. If a file is changed without a clear reason, stop and obtain an explanation or remove the change.
Check scope and provenance
Compare the patch with the baseline intent. Look for unrelated refactors, new telemetry, data collection, network calls, feature flags, permission changes, or modified build steps. Confirm that generated files are reproducible from their source and that no credentials, tokens, personal data, or private endpoints were added to logs, fixtures, comments, or test snapshots.
Step 4: Trace impact beyond changed lines
Follow callers, callees, and data flow
A diff shows what changed, not every invariant that the change can break. Identify callers of modified functions, functions they call, shared state, database transactions, queues, caches, and external services. Trace untrusted data from entry point to validation, parsing, storage, rendering, and outbound requests. Verify that authorization decisions are made on the correct identity and resource at every path, including error and retry paths.
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Review invariants explicitly
Write down properties that must always hold: a user cannot read another tenant’s record; a transaction cannot commit partially; a parser cannot allocate unbounded memory; a retry cannot duplicate a charge; a secret cannot appear in a response or log. Then inspect whether the generated code preserves each property in success, failure, timeout, cancellation, and concurrency scenarios.
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Apply a risk-based depth
Increase review depth for authentication, authorization, cryptography, parsing, deserialization, file uploads, dependency changes, deployment configuration, and pipeline changes. These areas can create severe consequences even when the patch is small. Have a security specialist or domain owner participate when organizational policy requires it.
Step 5: Challenge the tests instead of accepting coverage at face value
Look for weakened or misleading tests
- Deleted tests or reduced assertions.
- Mocks replacing the real database, authorization layer, queue, or network behavior that the requirement depends on.
- Snapshots or golden files updated only to match new output, without checking whether the output is correct.
- Tests that call private helpers or mirror the implementation’s branch structure so closely that the same mistake passes both code and test.
- Changed test configuration that excludes a directory, platform, race detector, coverage gate, or security check.
Add independent negative and boundary cases
Write at least a few tests from the requirement rather than from the generated code. Include missing fields, malformed encodings, unexpected types, empty and maximum-size values, duplicate requests, expired credentials, unauthorized identities, stale versions, partial downstream failures, and cancellation. For concurrent code, exercise races, ordering, retries, and idempotency. For parsers and interpreters, use malformed and ambiguous inputs. OWASP recommends adversarial or negative tests that are not generated by the same agent, because an agent can reproduce its own assumptions.
Use multiple test perspectives
NIST identifies functional or black-box tests, structural tests, historical regression tests, fuzz testing, and web-application scanning among complementary techniques. Functional tests ask whether externally visible behavior is right; structural tests exercise branches and conditions; historical tests prevent a known bug from returning; fuzzing explores unexpected input combinations. A passing suite provides evidence only for the paths and assertions it actually exercises.
Step 6: Interpret each evidence layer correctly
| Evidence | What it can establish | What it cannot establish alone |
|---|---|---|
| Build and compiler checks | That the selected targets compile, package, or type-check under the tested environment. | That runtime behavior, security properties, or requirements are correct. |
| Automated tests | Expected results for the cases, environments, and assertions they execute. | Untested requirements, missing failure modes, or correctness of weakened assertions. |
| Static analysis | Detectable bug patterns, data-flow findings, and coding-standard violations covered by configured rules. | Business-logic correctness, context-specific vulnerabilities, or safety of a clean scan. |
| Dependency and secret checks | Findings produced by the selected databases and patterns for the scanned files and versions. | Undiscovered vulnerabilities, intentional misuse, or secrets hidden from the scanner. |
| Diff review | A manageable view of edits and their apparent scope. | Invariants or side effects elsewhere in the system unless you trace affected flows. |
| Manual review | Context, design intent, business logic, and risks that tools cannot infer reliably. | Exhaustive proof that no defect exists. |
OWASP’s secure-code-review guidance describes manual review as a complement to automated security testing, especially for business logic, complex security implementations, and context-specific vulnerabilities. Treat every green result as a bounded observation, not a universal safety claim.
Step 7: Apply a human merge gate
Require qualified, independent ownership
OWASP’s AI Security Verification Standard 1.0, control AC.4.1, requires AI-generated code to be reviewed by a qualified human engineer. The reviewer must be separate from the identity that requested generation, and the AI agent does not count as the human reviewer. The reviewer should be able to explain the change, its assumptions, its failure modes, and why the collected evidence is appropriate.
Set explicit blocking rules
Define in repository policy which findings block a pull request. AISVS control AC.4.3 gives CVSS ≥ 9.0 as an example critical-finding threshold, or an organization’s equivalent threshold. Do not merge while a critical finding remains unexplained or formally accepted by the authorized risk owner. Record exceptions with scope, expiry, compensating controls, and the person accountable for the decision.
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Document the approval
Attach test and analysis results to the pull request, identify the reviewed commit, list known limitations, and note any checks that were not applicable. The approval should state what the reviewer inspected, not merely “LGTM.” If the reviewer cannot reconstruct the behavior or explain the risk, return the change for clarification, smaller commits, or additional tests.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical pre-merge checklist
- Intent: Read the issue, acceptance criteria, design documentation, and existing behavior; write the required outcomes and invariants.
- Baseline: Confirm the branch and commit, record pre-existing failures, and ensure unrelated working-tree edits are excluded.
- Build: Run the standard build or compile command and investigate every new warning and error.
- Tests: Run focused tests and the full required suite; inspect skips, flakes, fixtures, snapshots, and test configuration.
- Analysis: Run configured linters, type checks, static analyzers, and applicable security and dependency checks.
- Diff: Review every changed file, including lockfiles, workflows, configuration, tests, generated output, and instruction files.
- Scope: Remove or explain unrelated edits, new network calls, data collection, permission changes, and build-step changes.
- Impact: Trace callers, callees, data flow, shared state, error paths, retries, and invariants beyond the changed lines.
- Adversarial tests: Add independent negative, malformed, boundary, concurrency, authorization, and failure-injection cases where relevant.
- Risk decision: Escalate security-sensitive areas, resolve or formally accept findings, and block critical issues under policy.
- Human approval: A qualified reviewer separate from the generating identity explains the change and approves the specific commit.
Common failure modes and recovery actions
“All tests pass, so merge”
Why it fails: The suite may omit a requirement, assert the wrong behavior, or have been weakened by the patch.
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“The scanner is clean, so the code is secure”
Why it fails: Scanners recognize configured patterns; they do not reliably understand business rules, tenant isolation, or every context-specific flaw.
Recovery: Review data flow and authorization manually, run checks suited to the changed surface, and involve a security reviewer for high-risk paths.
“The generated summary lists only three files”
Why it fails: The patch may also alter lockfiles, workflows, tests, configuration, or instruction files.
Recovery: Generate the changed-file list from version control and inspect the complete diff and final tree.
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“The diff is small, so the risk is small”
Why it fails: A one-line parser, permission, dependency, or deployment change can affect a large call graph or expose a critical path.
Recovery: Trace callers, callees, data flow, and invariants; classify the surface before deciding review depth.
“The test changed only to reflect the new output”
Why it fails: Updating a snapshot or mock expectation can encode an incorrect implementation.
Recovery: Re-derive expected behavior from the requirement, add an independent assertion, and verify real integrations where the behavior matters.
“The same person requested generation and approved the merge”
Why it fails: It removes an independent check and conflicts with AISVS separation-of-duties guidance.
Recovery: Assign a qualified reviewer who did not request the generation and require that reviewer to document the reasoning and evidence.
When to stop and ask for a smaller change
Request decomposition when the patch mixes feature work with broad formatting, dependency upgrades, generated-code churn, or unrelated refactoring. Smaller commits make the diff auditable, allow focused tests, and reduce uncertainty about which edit caused a failure. Ask the author or agent to explain every unexpected file, assumption, and new external interaction before continuing.
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