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Review AI-generated code exactly as you would any other production change, but assume that plausible-looking code may contain untested assumptions, invented APIs, missed edge cases, or security flaws. A reliable review establishes the requested behavior, runs automated and functional checks, inspects the complete diff, traces sensitive data, verifies dependencies, evaluates long-term maintainability, and records approval by a responsible developer.
The sequence below turns those principles into a repeatable pull-request process. It applies whether an assistant wrote an entire feature, suggested a small function, or refactored existing code.
What makes AI-assisted code review different?
AI-generated code is not automatically defective, and it is not automatically safe because it compiles or passes a test suite. The main risk is misplaced confidence: generated output often looks idiomatic while solving a slightly different problem, relying on an API that does not exist, ignoring a project constraint, or omitting behavior that was never stated in the prompt.
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- Validate the change. Does it implement the requested behavior, including error paths and edge cases?
- Validate the engineering decision. Does it fit the architecture, security model, dependency policy, operational requirements, and expectations of future maintainers?
Tests, static analysis, dependency checks, and secret scanning provide valuable evidence. None of them proves that authorization is correct, that a business rule was interpreted properly, or that the change belongs in the system. Human contextual review remains necessary.
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Use this seven-stage review sequence
- Establish intent and context.
- Run functional and automated checks.
- Inspect the entire diff for correctness and omissions.
- Perform focused security review.
- Assess maintainability and operational fit.
- Verify dependencies and generated assumptions.
- Record accountable human approval before merge.
Run the stages in this order for ordinary changes. For code touching authentication, authorization, payments, personal data, file handling, network boundaries, or cryptography, start the security stage earlier and involve a specialist when your team requires one.
1. Establish intent and context before reading implementation details
Begin with the issue, acceptance criteria, design notes, relevant documentation, and the code surrounding the change. Read the request as a contract, not as a description of what the generated patch happens to do.
Define the behavior that must be true
- Write down the normal success path in observable terms: inputs, outputs, state changes, side effects, and user-visible errors.
- List boundary conditions such as empty values, duplicate requests, retries, timeouts, concurrent updates, maximum sizes, and unavailable dependencies.
- Identify non-functional requirements: latency, availability, audit logging, privacy, accessibility, compatibility, and data-retention rules.
- Separate requirements from assumptions. If the request does not specify a policy, find the policy in project documentation or ask the author rather than allowing generated code to invent one.
Check architectural fit
Compare the patch with nearby implementations and established conventions. A locally elegant function may bypass a service boundary, duplicate validation that belongs elsewhere, write directly to a database that normally sits behind a repository, or introduce a second configuration mechanism. Confirm that the selected layer owns the responsibility and that error handling follows the project’s existing pattern.
Ask the questions GitHub recommends
- What functional tests needed to validate this change do not exist or are missing?
- What possible vulnerabilities or security issues could this code introduce?
- Does the change align with the architecture?
- Which edge cases could be missed?
These questions are useful even when the generated patch is small. They force the review away from style and toward outcomes.
2. Run functional checks, then interpret the results
Use the project’s normal validation commands before drawing conclusions from the diff. Compile or build the affected targets, run focused tests, run the broader suite where practical, and inspect warnings as well as failures.
Minimum checks for a typical pull request
- Formatters and linters used by the repository.
- Type checking or compilation for every affected target.
- Unit tests for changed logic.
- Integration or end-to-end tests for changed boundaries, persistence, queues, or external services.
- Migration, contract, or compatibility checks when schemas or public interfaces change.
- Static analysis, dependency analysis, and secret scanning configured by the project.
A green result means only that the executed checks passed under their tested conditions. It does not establish that the requested feature was implemented, that untested branches are safe, or that a test itself was not deleted, weakened, or skipped by the patch.
Add tests for behavior the change introduces
Request tests when the patch adds a branch, changes validation, alters authorization, handles a new data shape, or changes retry and failure behavior. Tests should assert externally meaningful results rather than mirroring the implementation line by line. Include at least one negative case for each important rejection path and one boundary case for every limit or state transition.
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Investigate suspicious test changes
Look for removed assertions, broadened mocks, disabled suites, ignored errors, newly skipped cases, and snapshots updated without an explanation. Generated code can change a test until it passes while reducing what the test proves. Restore the original coverage or require a documented reason for the change.
3. Inspect the complete diff for correctness and omissions
Read the diff from top to bottom, then inspect the surrounding files. Do not review only the lines the assistant changed: configuration, routes, schemas, generated files, migrations, permissions, and tests may be affected indirectly.
Trace each requirement to an implementation and a check
| Review question | Evidence to locate | Warning sign |
|---|---|---|
| What triggers the behavior? | Route, command, event, job, or public method | A path exists but is never registered or invoked |
| What inputs are accepted? | Schema, parser, validation, and normalization | Validation occurs only in the client or after a side effect |
| What state changes? | Transaction, persistence call, cache update, or emitted event | Partial updates can leave inconsistent state |
| How does failure appear? | Typed error, status, retry policy, log, and user message | Exceptions are swallowed or sensitive details are returned |
| How is the behavior tested? | Focused, integration, and negative tests | Only the happy path is covered |
Look for generated-code failure patterns
- Hallucinated APIs: a method, option, import, or configuration key that is absent from the installed version.
- Incorrect logic: reversed comparisons, wrong units, timezone mistakes, stale state, or an assumption that a collection is non-empty.
- Ignored constraints: a requirement for idempotency, pagination, rate limiting, localization, auditability, or backward compatibility that is not reflected in the code.
- Unsupported edge cases: null values, malformed input, duplicate events, cancellation, retries, partial outages, and concurrent requests.
- Unintended surface area: a new endpoint, permission, dependency, log field, or data export that was not part of the request.
Review data transformations explicitly
For every conversion, document the source type, destination type, units, encoding, precision, and failure behavior. Generated code often performs an implicit conversion that works for the example in the prompt but fails for a real production value. Pay particular attention to dates, currency, identifiers, byte lengths, Unicode, and serialized formats.
4. Perform a focused security review
Security review should follow data and authority through the system. Automated scanners find repeatable patterns, but they commonly miss context-dependent authorization errors and business-logic flaws. Spend the most human attention on sensitive code and changes that cross trust boundaries.
Authorization and authentication
- Identify the authenticated principal and the resource owner for every operation.
- Verify authorization on the server-side operation, not only in a user interface or route wrapper.
- Check object-level access: a user who can access one record must not be able to substitute another identifier and read or modify it.
- Confirm role, tenant, scope, and state checks are applied consistently to reads, writes, exports, background jobs, and administrative actions.
- Ensure authentication failures, expired sessions, and token audience or issuer checks follow the project’s established policy.
Input validation and injection
Find every untrusted input and the sink it reaches. Validate type, length, format, range, and allowed values at the trust boundary. Use parameterized database operations, safe command APIs, context-appropriate output encoding, and vetted parsers instead of concatenating strings. Review query builders and templates too; an abstraction does not guarantee safe usage.
Cryptography and secrets
- Do not accept new cryptographic algorithms, modes, key sizes, salts, or randomness sources without checking the project’s approved guidance.
- Confirm passwords use the project’s password-hashing mechanism rather than a general-purpose hash.
- Check that keys and tokens are loaded from the approved secret store, never committed, logged, or returned in errors.
- Inspect comparisons, expiry, rotation, and replay protections for tokens and signed data.
Deserialization, uploads, and file paths
Review parsers for untrusted serialized data and confirm the accepted types are allow-listed. For uploads, verify size limits, content-type handling, filename normalization, storage location, malware scanning policy, and whether uploaded content can be executed or served with dangerous headers. For paths, prevent traversal and enforce an approved root after canonicalization.
Trust-boundary changes
Mark every transition between browser and server, service and service, application and database, or internal and external network. Ask what identity, integrity, confidentiality, and replay guarantees exist at each boundary. A generated retry, webhook handler, queue consumer, or data import can create a new boundary even when the diff looks like ordinary plumbing.
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Business-logic abuse cases
Test sequences, not just individual requests: repeated redemption, concurrent updates, changing an account identifier between checks, bypassing a required workflow step, or retrying an operation after a partial failure. Scanners are unlikely to understand these rules, so write explicit tests and review the state transitions manually.
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5. Assess maintainability and operational fit
Maintainability is the ability of another developer to understand, test, diagnose, and safely change the code later. Generated code can be verbose, repetitive, or over-engineered even when its output is correct.
Readability and structure
- Names should express domain meaning rather than implementation trivia.
- Control flow should make success, rejection, and failure paths visible.
- Functions should have one understandable responsibility and be small enough to test without elaborate setup.
- Remove duplicated branches and abstractions that do not reduce complexity.
- Comments should explain a non-obvious decision, invariant, or external constraint; do not retain comments that merely narrate the code.
Error handling and observability
Confirm errors are classified consistently, retried only when safe, and surfaced at the right boundary. Logs should contain enough context to diagnose a failure without exposing secrets or personal data. Metrics and traces should make new queues, retries, latency changes, and failure rates visible. If the patch adds a background operation, verify cancellation, timeout, idempotency, and dead-letter or recovery behavior.
Performance and resource use
Look for accidental N+1 queries, unbounded loops, loading entire files into memory, repeated network calls, missing pagination, and locks held across slow operations. Ask what happens at the documented maximum input, not only with the small example used to generate the code. Require a measurement or a reasoned bound when the change affects a hot path.
Compatibility and rollout
Check API and schema compatibility with older clients, workers, and deployed versions. For migrations, verify ordering, rollback or forward-recovery strategy, and behavior while old and new application versions run together. Feature flags, staged rollout, and data backfills should be explicit rather than hidden in generated initialization code.
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6. Verify dependencies and generated assumptions
Review every new import, package, image, action, model, and configuration key. Confirm that it exists in the version actually used by the project, is maintained, and has a license compatible with your distribution and policy.
Dependency checklist
- Is the package name and version real, and does its documented API match the code?
- Does the lockfile record the expected artifact and integrity value?
- Is the package actively maintained for the project’s runtime and platform?
- Does it bring transitive dependencies, native code, network access, or licenses that require review?
- Could an existing standard-library or approved internal component provide the same capability with less risk?
- Is the package name suspiciously similar to a known dependency, or does it come from an unexpected registry?
Do not accept an unfamiliar dependency merely because generated code imports it confidently. Ask the author to provide the official documentation, rationale, and license information, or replace it with a verified option.
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7. Layer automation instead of treating it as a verdict
A pull-request pipeline should combine controls that detect different failure modes. OWASP’s secure-review guidance recommends static application security testing, software composition analysis, and secret scanning on every pull request. Add format, type, unit, integration, and policy checks appropriate to the repository.
| Control | Best at finding | What it cannot establish | Review requirement |
|---|---|---|---|
| Tests | Regressions in specified behavior and known edge cases | Unspecified requirements, missing cases, or a flawed test oracle | Review coverage and assertions, not only pass/fail status |
| Static analysis | Known code patterns, type errors, data-flow issues, and policy violations | Business intent and many contextual authorization flaws | Triage findings and document accepted risk |
| Software composition analysis | Known vulnerable or disallowed dependencies and licenses | Unsafe use of a dependency or an undisclosed package source | Verify versions, reachability, and remediation plan |
| Secret scanning | Credentials and token-like material committed to repository history | Secrets constructed at runtime or exposed through logs and responses | Inspect configuration and operational paths manually |
| Human contextual review | Intent, architecture, authorization, abuse cases, and maintainability | Exhaustive detection of every syntactic issue | Require a named approver with relevant ownership |
Make findings visible on the diff and actionable. Configure the pipeline to run consistently on each pull request, cover the project’s languages and dependency ecosystem, and trigger additional review for sensitive paths or policy files. A clean scan is the beginning of review, not proof that the change is secure.
Keep human ownership explicit
A named developer must understand and approve an AI-assisted change before merge. The approval should be attributable in the pull-request record and tied to the person responsible for its security and maintainability. OWASP’s Human Accountability guidance states: “Every AI-assisted change should be reviewed, approved, and attributable to a developer who is responsible for its security and maintainability.”
Do not delegate accountability to the assistant, the author of the prompt, or a green CI status. If the reviewer cannot explain the change, its assumptions, and its failure modes, the review is not complete.
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- Read the request and acceptance criteria. Write a short statement of intended behavior and list explicit constraints.
- Map the change. Identify affected entry points, data stores, external services, permissions, migrations, configuration, and tests.
- Run baseline checks. Build the project and run the focused suite before making review conclusions.
- Read the diff twice. First for behavior and omissions; second for security boundaries, error handling, and maintainability.
- Trace sensitive flows. Follow untrusted input to sinks and principal identity to every protected operation.
- Challenge dependencies. Verify package existence, versioned APIs, maintenance status, licenses, and lockfile changes.
- Exercise edge cases. Add tests or manual cases for invalid input, retries, concurrency, limits, partial failure, and unauthorized access.
- Review automation findings. Resolve, suppress with a documented reason, or assign every actionable result.
- Record the decision. State what was checked, what remains uncertain, and who accepts the risk.
- Approve only when accountable. Merge requires the named human reviewer’s approval and all required checks.
Reviewer checklists by change type
API or web endpoint
- Authentication and object-level authorization are enforced server-side.
- Input schema, size limits, content types, and output encoding are explicit.
- Errors do not disclose secrets, stack traces, or other users’ data.
- Rate limits, idempotency, pagination, and timeout behavior match the endpoint’s risk.
- Contract tests cover success, validation failure, unauthorized access, and dependency failure.
Database or migration change
- Constraints, indexes, transaction boundaries, and nullability match the domain rules.
- Existing rows and concurrent writers are handled safely.
- Old and new application versions can coexist during deployment.
- Rollback or forward-recovery steps are documented and tested where feasible.
- Queries are parameterized and do not expose tenant or private data.
Authentication, authorization, or cryptography
- Identity, role, scope, tenant, expiry, and revocation checks are explicit.
- Approved algorithms, key storage, randomness, rotation, and replay controls are used.
- Negative tests cover missing, expired, forged, and cross-account credentials.
- Logs and metrics avoid tokens, passwords, and sensitive personal data.
File, import, or message processing
- Parser and content limits prevent resource exhaustion.
- Formats and fields are allow-listed and validated before side effects.
- Paths, storage, execution permissions, and output headers are safe.
- Retries are idempotent and malformed messages are isolated for diagnosis.
Common review failures and how to recover
“It passed all tests, so it is correct”
Return to the acceptance criteria and create tests for untested requirements and edge cases. Verify that the test suite still asserts the behavior it claims to cover.
“The scanner found nothing”
Perform manual authorization and business-logic review, trace trust boundaries, and inspect dependency use. Treat scanner output as coverage of specific patterns, not a security certificate.
“The generated code is too large to understand”
Ask for decomposition into small, testable units and removal of unused abstractions. A reviewer should be able to explain the control flow and state changes without relying on the assistant’s explanation.
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“A new package fixes the problem quickly”
Pause the merge, verify the package and its API in authoritative documentation, inspect its license and transitive dependencies, and determine whether an approved existing component is sufficient.
“The patch changed unrelated files”
Split unrelated work into a separate change or require a clear rationale. Unrelated formatting, generated output, configuration edits, or test rewrites increase the chance that a security-relevant change is missed.
“The reviewer cannot reproduce the environment”
Document runtime, database, feature-flag, and external-service assumptions; add a deterministic test or a reproducible setup; and do not approve behavior that has not been understood.
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What a useful review record contains
The pull-request record should let a later maintainer understand why the change was accepted. Capture:
- The requirement or issue being implemented.
- Checks run and their results, including the relevant test scope.
- Security-sensitive paths reviewed and any assumptions about trust or authorization.
- New dependencies, version and license verification, and any accepted advisories.
- Known limitations, deferred tests, rollout conditions, and recovery steps.
- The name of the developer who reviewed and approved the change.
Keep the record concise but specific. “Looks good” does not show what was evaluated or who accepted the residual risk.
The standard to apply before merge
Merge AI-assisted code only when a human can connect each important requirement to implementation and evidence, explain how sensitive data and authority flow through the change, verify every dependency and generated assumption, and describe how the code will be maintained and operated. Automated checks should run consistently and block known policy violations, while human review decides whether the change is actually correct for the project.
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