How to Review AI-Generated Code Before Merging

How should I review AI-generated code before merging it

Review AI-generated code the same way you review risky human code: start with behavior, then security, then maintainability, then ownership of the change. The AI can speed up drafting, but it does not carry review responsibility. You do. GitHub’s review guidance for AI-generated code starts with functional checks and then moves into context, quality, dependencies, and AI-specific pitfalls. OWASP’s secure code review guidance adds the manual inspection step that tools miss.

Begin with the smallest question: does it compile, test, and do the intended job Run the relevant unit, integration, and end-to-end tests before you read style comments. GitHub recommends starting with functional checks, including compilation, test execution, static analysis, and dependency scanning. If a change fails here, stop and fix the behavior before arguing about naming or structure.

Read the diff as if you were going to own the code next month. That means checking what changed, what did not change, and what assumptions the code now depends on. Google’s review guidance says technical facts override opinion, and that design decisions should be judged on engineering principles rather than preference. If the AI picked a pattern that is unfamiliar, demand the reason in the code or the pull request description.

Check the code against the project’s actual architecture, not the model’s idea of a clean solution. AI often writes something that works in isolation but fights the surrounding codebase. Look for duplicated helpers, new abstractions that do not match existing patterns, and dependencies that solve one issue while creating three more. Google’s style guidance says local consistency matters only when it does not worsen the codebase or spread the problem further.

Security review deserves its own pass. Look for input validation, output encoding, authentication, authorization, secrets handling, and unsafe file or network operations. OWASP explicitly lists these areas because automated checks miss context-specific flaws and business logic mistakes. A model can produce code that looks tidy while quietly trusting user input, widening permissions, or leaking data in error paths.

The part people get wrong is assuming tests are enough. Tests tell you what the code currently does, not whether it should do it, whether the behavior is safe, or whether the change creates a maintenance trap. A reviewer still needs to follow data flow, check boundary cases, and confirm that error handling does not reveal sensitive details. OWASP’s review checklist and Google’s reviewer guidance both treat human judgment as the core of review, not an optional final step.

Pay attention to changes that are technically correct but operationally expensive. AI-generated code often adds convenience wrappers, extra layers, or new dependencies that make later debugging harder. Ask whether the change can be explained to the next person who touches it without reverse engineering the prompt. Google’s style guide stresses that comments should explain why something exists, not restate what the code already says. If the code needs long explanation to make sense, the structure is probably too clever.

Review dependencies and generated helpers as if they were supply-chain decisions. New packages, new SDK calls, and copied snippets can bring in licensing, maintenance, or security issues that do not show up in the diff itself. GitHub’s guidance specifically calls out dependency scrutiny when reviewing AI-generated code. If the change adds a library, verify why that library is necessary, whether it is already in use elsewhere, and whether the version matches project policy.

Use a structured checklist every time. Confirm the code matches the ticket, the tests cover the new behavior, the failure paths are explicit, the logging is useful but not leaky, and the naming matches the project. Then ask one practical question: if this breaks in production, can the on-call person understand it fast enough to fix it That question catches a lot of AI output that passes casual review but fails real operations.

Read comments and documentation carefully. AI often invents comments that sound helpful but are stale, redundant, or plain wrong. Google’s review guidance says comments should explain why code exists, and README guidance says project docs matter especially for first-time users. If the code changes behavior, update tests and docs in the same merge so the next reviewer is not left reconciling three versions of the truth.

Do one review pass with the AI out of the room, so to speak. Read only the patch, not the prompt, and ask what a future maintainer would infer from the code alone. Then do a second pass with the intent in mind, to catch mismatches between the request and the result. GitHub recommends reviewing the generated change yourself before moving on, and then using collaborative review for anything that touches important logic.

When the code touches authentication, authorization, money, data export, or user-visible side effects, escalate the review. These are not places to trust a plausible-looking draft. Use a second human, make the tests stricter, and require a rollback plan. OWASP’s secure review guidance treats business logic and complex security implementations as exactly the kinds of places where manual analysis matters most.

The inconvenient part is that a good review sometimes means deleting the AI-generated code and writing a smaller change. That is normal. If the safest version is less clever, less abstract, or less complete, choose that version. A merge is not a reward for using AI, it is an acceptance that the code is ready for your team to maintain. GitHub’s review guidance and Google’s reviewer standards both push toward facts, clarity, and maintainability over novelty.

If you want a practical order of operations, use this: run tests, inspect the diff, check for security issues, compare against local patterns, verify docs and comments, then ask one teammate to look at the highest-risk part. That sequence keeps the review grounded in evidence and avoids spending time polishing code that should not merge at all. It also makes AI useful for drafting without letting it set the standard for acceptance.

For teams building with DevConnect, keep the same discipline: review the code, not the tool. If you are comparing workflows, the platform overview is here: https://devconnectplatform.com. Use it for the exchange around testing, not as a substitute for code review. The merge decision should still be based on what the code does, what it breaks, and who will maintain it next.

What should I check first in AI-generated code

Start with behavior. Run the tests and confirm the code compiles or at least reaches the same level of green that your normal merge rules require. GitHub’s guidance puts functional checks first because they catch obvious failures early, before anyone spends time debating design.

What mistakes do AI-generated changes make most often

They often look correct while missing context, especially around edge cases, security boundaries, and project-specific conventions. That is why manual review matters even when the code reads cleanly. OWASP and Google both emphasize human judgment for context, business logic, and design.

Should I accept AI-generated code if tests pass

No. Passing tests are necessary, not sufficient. You still need to inspect security, dependencies, maintainability, and whether the change matches the task. A test suite can confirm existing behavior and still miss a dangerous or brittle implementation.

What should be updated when the AI code changes behavior

Update tests, comments, and docs in the same merge. Google’s guidance says comments should explain why the code exists, and README guidance says documentation is critical for users and maintainers. If behavior changes and the docs do not, the next reviewer inherits confusion.

When should I ask for a second reviewer

Ask for a second reviewer on security-sensitive, money-related, authentication, or externally visible changes. Those are the places where a confident-looking draft can still fail in production. OWASP identifies those as areas where manual inspection finds issues that tools and quick scans miss.