Verify AI-generated code exactly as you would any production change, but make several failure modes explicit: establish the intended behavior first, inspect the complete diff, run deterministic build and security checks, test the tests, validate every API and dependency, and obtain qualified human approval. A green test run is evidence about the cases that ran—not proof that the change solves the right problem or is safe to deploy.
What verification must establish
An AI assistant can produce code that compiles, follows familiar patterns, and still violates a business rule, calls an API that does not exist in your library version, introduces an unsafe dependency, or tests only the implementation’s own assumptions. Verification therefore has two distinct jobs:
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- Functional evidence: show that the code builds and behaves correctly for specified scenarios, including important failures and boundaries.
- Engineering judgment: determine whether the change matches the requirement, architecture, security model, operational constraints, and maintenance standards.
Use the same merge and deployment controls required for human-written code. Add AI-specific checks to the review because generated code can look plausible while containing hallucinated APIs, ignored constraints, fabricated packages, incorrect logic, or tests that were weakened, skipped, or deleted.
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Start with a written contract
Before reading the implementation, write down what success means. This prevents a generated solution from defining its own specification through its code and tests.
Record required behavior and constraints
- State the user-visible or system behavior that must change.
- List acceptance criteria in observable terms, including expected outputs, errors, timeouts, and side effects.
- Identify compatibility requirements such as supported language, runtime, database, browser, operating system, or API versions.
- Define security boundaries: who may call the code, which data is sensitive, what must be validated, and what must never be logged or exposed.
- Specify non-functional constraints that matter to the task, such as latency, resource limits, idempotency, migration safety, accessibility, or backward compatibility.
- Identify behavior that must not change.
Map the project context
Find the existing implementation, related tests, architecture documentation, coding conventions, build instructions, deployment configuration, and dependency policy. A test suite checks only what it encodes. If the requirement is absent from the suite, a generated change can pass every existing test while solving the wrong problem.
Ask which assumptions are business decisions rather than implementation details. For example, whether an expired session should be rejected, refreshed, or treated as anonymous is a product and security decision. Do not let the assistant choose it silently.
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Read the full diff rather than relying on an assistant’s summary or pull-request description. Include files that are easy to overlook because they can change how the code executes.
Review every touched area
- Application code and control flow, including new branches and error paths.
- Tests and fixtures, especially assertions that were changed, removed, weakened, or marked as skipped.
- Configuration, environment variables, feature flags, permissions, and deployment manifests.
- Build scripts, code-generation steps, database migrations, serialization formats, and public interfaces.
- Dependency manifests, lockfiles, package registries, and transitive dependency changes.
- Logging, telemetry, caching, retries, queues, and resource-management code.
Look for security and reliability hazards
- Hard-coded credentials, tokens, private keys, or secrets copied into fixtures or logs.
- Unvalidated input reaching SQL, shell commands, templates, file paths, HTML, or deserialization routines.
- Authorization checks applied after a data fetch, applied to the wrong identity, or absent on a new endpoint.
- Unbounded loops, memory growth, concurrency races, retry storms, and denial-of-service paths.
- Fail-open behavior, swallowed exceptions, unsafe defaults, and misleading success responses.
- Personal or regulated data written to logs, analytics, caches, or error messages.
Generated code often expands the scope of a task by adding helper layers, configuration, or packages that were not necessary. Every extra surface increases what must be maintained and secured; ask the author to remove changes that do not support the contract.
Run deterministic checks in the intended environment
Use the repository’s documented commands and the environment in which the code is meant to run. Do not infer that a command succeeded merely because it returned a familiar-looking output; inspect warnings, skipped work, and the final exit status.
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Build and test the change
- Install the repository’s pinned toolchain and dependencies, or use the project’s documented container or build environment.
- Compile or build the affected targets. A build failure is a release blocker; do not waive it because the assistant claims the code is illustrative.
- Run the full relevant test suite, not only tests generated alongside the change.
- Run focused tests for each changed behavior and for likely regressions in neighboring components.
- Repeat checks that depend on configuration, database state, permissions, or external services in a representative setup.
Use the project’s own commands. Typical examples include npm test, pytest, go test ./..., or cargo test, but the correct command is whatever the repository documents for its supported version and environment. Do not substitute a convenient local command that silently omits integration tests or platform-specific checks.
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- Type checking and compilation with warnings enabled where the project supports them.
- Linting, formatting, and static analysis required by the repository.
- Vulnerability scanning for source and built artifacts.
- Dependency and license checks, including transitive packages.
- Infrastructure, container, secret, and configuration scans where applicable.
- Coverage reporting as an inspection aid, not as a universal correctness threshold.
GitHub recommends automating CI checks for style, security, quality, and coverage, while also using human review. A successful run means that the executed checks passed under that test set and environment. It does not establish that untested behavior, production configuration, hidden requirements, or security assumptions are correct.
Audit the tests as carefully as the production code
AI-generated tests are code and can encode the same misunderstanding as the implementation. Read them line by line.
Check that assertions distinguish right from wrong
- Each test should fail when the intended behavior is broken, not merely execute a function without checking a meaningful result.
- Assertions should verify exact error types, status codes, state transitions, or side effects where those details are part of the contract.
- Tests should cover normal, boundary, malformed, unauthorized, unavailable, and timeout cases that the requirement makes relevant.
- Fixtures and mocks should represent behavior that can occur in production; a mock that always returns success can hide failure handling.
- Tests should exercise independent inputs rather than repeating the same example with different names.
Detect tests that protect the implementation instead of the requirement
- A test asserts the assistant’s chosen algorithm or private helper rather than observable behavior.
- A mock is configured to return the exact value produced by the implementation, making an incorrect transformation pass.
- Assertions are broadened from exact values to “not null,” “truthy,” or a status range without a documented reason.
- Tests are skipped, deleted, or marked as expected failures instead of fixing the underlying issue.
- Property, fuzz, or parameterized tests use an input generator that never reaches important boundaries.
Ask a reviewer who knows the requirement to describe a plausible bug that each test would catch. If the test would remain green for that bug, add or rewrite the assertion. Do not impose a single coverage percentage across projects: coverage shows which lines ran, while only review can determine whether the exercised behavior meaningfully validates the contract.
Validate APIs, packages, and generated assumptions independently
Confirm every API against the exact version
For each newly used function, class, flag, protocol field, or configuration key, verify that it exists in the exact runtime or library version declared by the project. Check the official documentation and a small, direct usage example. Watch for near-miss names, methods available only in a newer release, parameters with different defaults, and APIs that are asynchronous or stateful when the generated code treats them as synchronous or stateless.
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Vet every new dependency
Before adding a package, verify its package name, publisher or maintainer, source repository, release activity, transitive dependencies, license compatibility, and security history. Confirm that the dependency is not a typo-squatted or abandoned package and that its maintenance policy fits the life of your system. GitHub specifically warns reviewers to investigate suspicious or nonexistent packages and to check origin, maintenance, and licensing.
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Reproduce critical assumptions
For a security-sensitive or high-impact API, make a minimal test or local reproduction that demonstrates the documented behavior. This catches fabricated endpoints and incorrect parameter ordering faster than a large integration test. Record the version and configuration used so another engineer can repeat the check.
Use layered verification instead of a single pass
| Layer | What to do | Problems it can reveal | What it cannot prove |
|---|---|---|---|
| Build and type checks | Compile the affected targets with the supported toolchain. | Syntax errors, missing symbols, type mismatches, incompatible generated code. | Correct business behavior, secure defaults, or production configuration. |
| Unit and integration tests | Run existing tests plus focused cases for changed behavior and regressions. | Known functional failures and some integration defects. | Requirements that no test expresses or scenarios not represented by the environment. |
| Static analysis | Run the project’s linters, type analyzers, and data-flow checks. | Suspicious control flow, unsafe patterns, style violations, some injection paths. | Whether the chosen behavior is what users and owners intended. |
| Security and dependency scans | Scan source, artifacts, manifests, and transitive packages. | Known vulnerabilities, leaked secrets, risky licenses, and dependency risk signals. | Unknown vulnerabilities or authorization logic that is semantically wrong. |
| Human review | Compare the diff with requirements, architecture, and operational context. | Wrong assumptions, missing edge cases, needless scope, trade-off and maintainability problems. | Exhaustive detection of every defect; reviewers need evidence from the other layers. |
These layers are complementary. A tool that reports no issue has completed its analysis; it has not certified the absence of defects.
Require qualified review and preserve provenance
A reviewer who understands the system should approve that the change solves the task, fits the architecture, respects sensitive boundaries, and can be maintained. Add a second reviewer or specialist review for authentication, payments, privacy, infrastructure, migrations, concurrency, or other high-consequence changes.
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Keep the generated change visible through ordinary commits, pull requests, review comments, CI records, and release evidence. Record which assistant or agent produced material changes when your organization’s policy requires it, along with the prompts or issue context needed to understand provenance. Do not replace normal ownership with a label such as “AI-generated.”
The UK Home Office Engineering Guidance and Standards, SEGAS-00020, last updated 20 March 2026, states: “AI tools cannot replace human judgement, understanding, ownership, or responsibility for decisions, designs, or changes made to systems.” That is a departmental engineering standard for its own teams, not a universal legal rule, but it expresses the accountability needed for production software. The same standard requires suitably qualified human review and approval before production, testing before merge or deployment, and traceability through standard engineering processes.
How to use benchmarks without mistaking them for proof
A benchmark measures performance on a defined dataset and evaluation procedure. It can help compare systems under controlled conditions, but it does not guarantee that a particular generated patch is correct in your repository.
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Compare the evaluation, not just the score
| Question | Why it matters |
|---|---|
| Are the tasks representative? | A benchmark of isolated bug fixes may say little about your monorepo, languages, deployment constraints, or domain rules. |
| Were tests written independently and validated? | Flawed or underspecified tests can reject correct solutions or accept incorrect ones. |
| What environment and versions were used? | Toolchains, dependencies, operating systems, network access, and resource limits can change outcomes. |
| How many runs and what metric? | One pass rate hides variance, partial credit rules, timeout treatment, and the difference between fixing code and passing tests. |
| Could training data contain the tasks or fixes? | Exposure can inflate a score without demonstrating generalization to unseen work. |
| What security and dependency checks were included? | Functional success alone can miss vulnerable code, unsafe packages, or license conflicts. |
| Does the result match your acceptance criteria? | A model can score well on benchmark tasks while failing requirements that matter to your team. |
Report the benchmark name, version or evaluation date, model and tool version, environment, number of runs, metric, and known limitations together. Keep those qualifications attached whenever the result is presented.
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OpenAI also reported that the frontier models it tested could reproduce original human-written fixes or details from problem statements for some SWE-bench tasks. It interpreted this as evidence that some evaluated problems or solutions had appeared in training data, concluding that Verified scores increasingly reflected training exposure and recommending SWE-bench Pro for new evaluations. Those are OpenAI’s findings and recommendation, not settled independent consensus. Treat any benchmark score with the same scrutiny: inspect task provenance, test quality, and contamination risk before drawing conclusions.
Build an internal evaluation
- Draw held-out tasks from your team’s own completed work where feasible, removing reference implementations and sensitive data.
- Write acceptance tests before running the candidate assistant or agent.
- Include functional, regression, failure-mode, security, and dependency checks appropriate to the task.
- Keep task wording, tool access, model version, runtime, and resource limits constant when comparing systems.
- Report failures and partial outcomes, not only the pass rate.
- Have engineers inspect a sample of successful and failed results, and record defects in the tests themselves.
This approach measures usefulness for your acceptance criteria rather than substituting a public leaderboard for engineering evidence.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What AI code review can and cannot tell you
An automated reviewer can prioritize suspicious lines and suggest fixes, but its comments need validation. Silence is not evidence that a change is safe, and a plausible comment is not automatically correct.
Interpret operational results carefully
OpenAI’s 1 December 2025 article described an internal study and deployment of Codex code review. In the organization’s described cloud workflow, comments appeared on 36% of pull requests that were entirely generated by that workflow; 46% of those comments led to an author code change. The article compared that with a 53% change rate for comments on human-generated pull requests and reported that 52.7% of comments were addressed with code changes across the described deployment.
These are organization-specific operational measures. They are not universal reviewer accuracy, recall, precision, or correctness rates. A code change made in response to a comment does not prove that the comment was right, and an ignored comment does not prove it was wrong.
Understand the evaluation’s limits
OpenAI said its controlled inference-budget study used comments that humans had already verified. Because the evaluation began with known human findings, it cannot establish the correctness of novel issues without additional human input. The authors also reported a faster performance drop as reasoning budget was reduced for reviews of generated code than for reviews of human-written code. Keep those limitations beside the results when deciding how much autonomy to grant an automated reviewer.
Use AI review as an additional pass after deterministic checks and before human approval. Ask the reviewer to identify missing tests, security boundaries, dependency risks, and requirement mismatches, then require a human to reproduce and accept each actionable finding. GitHub likewise advises using Copilot code review to supplement careful human review and recommends teammates for complex or sensitive changes.
Common AI-specific failure modes and responses
| Failure mode | How it appears | Verification response |
|---|---|---|
| Hallucinated API | A method, option, or protocol field has a convincing name but is absent or different in the pinned version. | Check official versioned documentation and build a minimal reproduction before accepting the call. |
| Fabricated or risky dependency | A package is obscure, newly published, typo-similar, abandoned, or unrelated to the claimed capability. | Verify origin, maintainer, release activity, transitive dependencies, license, and security status; remove it if the standard library or existing dependency suffices. |
| Requirement drift | The code handles a nearby problem, changes behavior outside the task, or chooses an unstated business rule. | Return to the written contract and inspect affected callers, data flows, and acceptance tests. |
| Happy-path testing | Tests pass for valid input but omit authorization, malformed data, timeouts, retries, empty results, or partial failure. | Add boundary and failure cases derived from the contract and threat model. |
| Test suppression | A failing test is deleted, skipped, weakened, or hidden behind a broad mock. | Restore the test, investigate the failure, and require an explicit, reviewed reason for any skip. |
| Unsafe defaults | Debug logging, permissive access, weak validation, unlimited retries, or plaintext secrets appear in a generated helper. | Review configuration and failure paths under production-like settings; run security scans and targeted tests. |
| Overconfident review result | An assistant labels a patch safe because checks are green or reports no findings. | Use the result as a pointer, not an approval; complete requirement, security, and maintainability review. |
A practical merge and deployment gate
Before merging, the responsible engineer should be able to answer yes to each applicable question:
- Is the required behavior and the explicitly forbidden behavior written down?
- Does the complete diff contain only task-related changes?
- Does the code compile or build with the supported toolchain?
- Did the full relevant suite and focused regression tests run in the intended environment?
- Do the tests assert the requirement, including meaningful boundaries and failures?
- Were skipped, deleted, weakened, or newly generated tests examined?
- Did type, lint, static-analysis, security, and dependency checks complete, with warnings investigated?
- Was every new API verified against the exact library or runtime version?
- Was every new package checked for origin, maintenance, transitive risk, license, and security?
- Did a qualified reviewer assess intent, architecture, authorization, data handling, and operational trade-offs?
- Are the change, review, test evidence, and approval traceable through normal engineering records?
If any answer is no, the next action is to close that evidence gap—not to rely on the assistant’s confidence, a leaderboard score, or a clean automated-review summary.
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