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Review the code in the submitted change—not an earlier model draft. First establish what the patch is meant to do, then inspect its scope, follow the highest-risk code paths, and verify behavior with tests and suitable analysis. Whether a model wrote the first draft, a person changed it, or the history is incomplete, authorship guesses do not establish correctness.
How do I review AI-generated code?
Use the same core standard as for any change: the final diff must match the intended behavior and project requirements. A model’s proposal can provide context if it is available and clearly connected to the submission, but it is not a substitute for examining the patch that will be merged.
1. Establish the change’s contract
- Ask what behavior should change and what must stay the same.
- Clarify assumptions, affected users or data, and expected failure behavior.
- If known, ask which parts were generated, rewritten, or manually edited.
- Compare the explanation and test plan with the actual submitted diff.
If the original model output was not retained, do not assume it can be reconstructed. Review the available change and use repository history or logs only to the extent they actually preserve the relevant edits.
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Identify the files, components, dependencies, data flows, and behaviors touched. Look for scope mismatches: unrelated cleanup, unexplained files, a missing migration or rollback, or tests that do not cover the described behavior. JetBrains Research’s 2026 proposed framework recommends moving from a high-level view to selective inspection of files and code snippets; it draws on a participatory design study with 17 practitioners and a follow-up survey of 43 software professionals, rather than proving a universal defect-reduction result. Read the framework.
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3. Spend review time where failure matters
Prioritize the parts of the change that carry meaningful consequences. Depending on what the patch touches, trace authentication and authorization, data access, input validation, error handling, concurrency, persistence, external calls, and security-sensitive configuration. Check whether new dependencies and generated files are expected, and whether the implementation fits relevant project conventions.
4. Verify behavior independently
Run focused tests and inspect what they assert. A passing suite is useful evidence, not proof: check that tests exercise the intended behavior, important edge cases, and failure conditions. Apply static analysis and security checks where appropriate, then verify automated findings against the code and its intent.
Automated review can add another signal, but false alarms and missed issues both matter. OpenAI describes its code-review system as complementing other oversight and discusses balancing signal quality against recall and false positives. Its reported results are observations from its own deployed system, not an independent benchmark: the reviewer commented on 36% of PRs entirely generated by Codex cloud, and 46% of those comments led to a code change. Across comments from the deployed reviewer, authors addressed findings with code changes in 52.7% of cases. OpenAI’s account of code verification.
What if the code changed after the AI generated it?
Treat the final submitted patch as authoritative. A model’s earlier draft may explain a design choice, but only when the draft is available and tied to the change. Human edits can correct, extend, or introduce defects; the final code still needs to satisfy the task and pass independent review.
When accountability or later incident analysis requires provenance, preserve a useful record in the pull request or an approved audit trail. It can identify the tool or agent, the task or intent, the responsible human owner, and material follow-up edits. The specific mechanism should follow team policy and repository tooling. GitLab’s accountability framing asks where code came from, what it was meant to do, and who remains responsible after deployment; Bukhari, Tan, and De Carli describe generated code as a software supply-chain provenance concern. GitLab’s 2026 report announcement · The 2023 code-origin study.
How can I tell if code was written by AI?
You generally cannot establish authorship reliably from style alone. In a 2026 Harris Poll survey of 1,528 developers and technology buyers across six countries, 43% of respondents said they could not reliably distinguish AI-generated code from human-written code in their codebase. That is a self-reported survey result, not an audit of code or a test of any reviewer’s ability.
A 2023 study reported up to 92% classification accuracy under its ideal-condition evaluation on a selected, cleanly labeled dataset. That controlled feasibility result does not make a classifier a field-ready detector or establish who wrote a particular production line. Treat detection as, at most, a triage clue—not proof of provenance or correctness. See the study and its conditions.
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It can assist a review, but it should not replace a human’s understanding of the change or become sign-off by itself. Verify each finding in context, consider what the tool may have missed, and keep responsibility for validating the final patch with the team’s human reviewers.
Best Value
Survey findings suggest review workload is a real concern, but they measure perceptions rather than universal outcomes. In GitLab’s 2026 survey, 85% of respondents agreed that AI had shifted the bottleneck from writing code to reviewing and validating it. These figures are not audited measures of time or defect rates. GitLab’s survey announcement.
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