AI-Written Code: What to Check Before You Merge

Before merging AI-written code, review it as carefully as any other contribution: confirm it meets the requirements, understand every changed line, test important behavior, and check security, dependencies, and maintainability. Passing automated tests is useful evidence, not proof that the code is correct or safe.

Start with the change and its requirements

First establish what the code is supposed to do and compare that with what it actually changes. AI-generated patches can be plausible while solving a nearby problem, missing an edge case, or modifying files outside the request. Read the issue, acceptance criteria, and relevant design notes before judging the implementation.

• Inspect the complete diff, including configuration, generated files, migrations, and lockfiles—not only the main source file.
• Check that each change is necessary and that unrelated formatting or refactoring has not obscured the functional patch.
• Look for omitted requirements, such as input validation, error handling, compatibility, logging, or accessibility.
• Confirm the behavior at boundaries: empty input, missing values, unusual but valid values, and failure paths.

For example, a function that “returns the first matching record” may work for a typical list but fail when the list is empty or when two records match. The specification determines which result is correct; code that merely looks reasonable does not.

Understand the implementation before approving

A reviewer should be able to explain the important control flow and why the chosen approach fits the project. Treat unfamiliar code as a prompt to investigate, not as something to approve because it is neatly formatted or confidently described. AI-generated comments and explanations can also be wrong or out of date.

• Trace data from entry point to output, especially across new helper functions and service boundaries.
• Check assumptions about types, nullability, ordering, time zones, character encoding, and shared state.
• Compare the patch with nearby patterns in the codebase, while verifying that those patterns are still appropriate.
• Remove dead code, redundant abstractions, speculative features, and comments that simply restate the code.

If a block is difficult to understand, ask for a clearer implementation or documentation, or investigate it independently. Do not rely on a generated explanation as verification. If you cannot determine what a security-sensitive or business-critical change does, it is not ready to merge.

Verify behavior with tests and tools

Run the checks the project expects, using the repository’s documented commands and supported environment. Depending on the project, these may include unit and integration tests, type checking, linting, formatting, static analysis, or a build. Check that the results correspond to the current patch; a stale successful run or a test suite that did not execute is not a pass.

• Run existing tests relevant to the modified behavior, then add tests for new behavior and important edge cases.
• Use regression tests to show that a reported bug is fixed and stays fixed.
• Check that tests assert meaningful outcomes rather than merely exercising lines or duplicating the implementation.
• Review failures rather than dismissing them as unrelated without evidence; investigate flaky tests separately.

Tests can miss defects when they cover only the expected path or repeat the same mistaken assumption as the implementation. For example, a test that checks a successful database lookup does not establish that missing records are handled correctly. Combine tests with code review, and use a small manual or integration check where that gives useful additional evidence.

Review security, privacy, and dependencies

Examine how the patch handles untrusted input and sensitive information. Common risks include injection, unsafe deserialization, weak authorization checks, accidental exposure of secrets, and logging personal or confidential data. Verify that permissions are enforced on the server or trusted boundary, not only hidden in the user interface.

• Check queries, shell commands, templates, file paths, and markup for safe handling of user-controlled values.
• Confirm that authentication and authorization rules match the intended access, including denial cases.
• Look for credentials, tokens, private data, or overly detailed error messages in code, tests, logs, and examples.
• Review new packages, version changes, install scripts, and license or maintenance implications under the project’s dependency policy.

A package name or API suggested by a code generator should not be assumed to exist or be trustworthy. Confirm the dependency from the project’s normal sources and inspect the lockfile changes. If the patch affects a sensitive system, follow the organization’s security review process; a green scanner report does not replace judgment.

Check integration and long-term maintenance

Code must fit the application around it. Review API and data compatibility, configuration defaults, error propagation, performance, and the effect on existing users or stored data. A locally correct function may still break a caller that depends on an older return shape or a database migration that cannot safely run in the deployment order.

• Check public interfaces, serialized formats, database schemas, and migrations for backward compatibility.
• Consider resource use and scaling: repeated network calls, unbounded loops, large in-memory data, and unnecessary database queries.
• Verify that configuration and feature flags have sensible behavior when unset, invalid, or disabled.
• Ensure observability is useful without leaking sensitive values, and that failures are surfaced rather than silently ignored.

When the impact is broad, request review from someone familiar with the affected subsystem. Smaller, focused commits are easier to reason about and revert if a problem appears. A generated patch does not need a special process solely because of its origin, but uncertainty, complexity, and risk should determine how much review it receives.

Common review mistakes and a practical workflow

Two misconceptions cause avoidable problems: that passing tests mean the patch is correct, and that AI-written code is inherently either safe or unsafe. Tests provide limited evidence about tested cases; the source of a patch does not determine its quality. Evaluate the actual change and its consequences.

1. Read the request and define the expected behavior.
2. Inspect the full diff and identify affected callers, data, and configuration.
3. Trace the implementation and challenge its assumptions and boundary cases.
4. Run relevant tests and tools; add tests for gaps that matter.
5. Review security, dependency, compatibility, and operational effects.
6. Resolve unclear behavior, unexplained changes, and meaningful failures before approval.

For a low-risk documentation typo, this may be quick. For authentication, payments, data migrations, or other consequential areas, it may require deeper tests and specialist review. The goal is not to prove that no defect is possible; it is to gather appropriate evidence and understand the remaining risk before merging.

Frequently Asked Questions

Should AI-written code get extra review?
Review it according to complexity and impact, not authorship alone. Extra scrutiny is sensible when the patch is hard to understand, changes sensitive behavior, or introduces unfamiliar dependencies.

Are passing tests enough to merge AI-generated code?
No. Tests only cover the cases they exercise and can share the implementation’s assumptions. Review the diff, requirements, edge cases, and security implications as well.

How can I tell whether generated code is wrong?
Compare it with explicit requirements, trace its data and control flow, and test boundary and failure cases. Verify unfamiliar APIs and dependencies against reliable project or package documentation.

Should I disclose that code was generated?
Follow your team’s contribution and disclosure policies. Regardless of disclosure, reviewers need a clear description of the behavior, relevant risks, and tests performed.

What if I cannot understand part of the patch?
Do not approve that part on trust. Ask for clarification or a simpler implementation, inspect the relevant documentation, and involve a maintainer with the needed context.

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