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Review the patch that was actually submitted—not an earlier model draft, and not assumptions about who wrote each line. Start by agreeing on the intended behavior, inspect the final diff, then test the relevant code paths and scrutinize the results. If the patch changed after generation, available version history can clarify what changed; without a saved record, you may not be able to reconstruct every intermediate draft.
How do I review AI-generated code?
Use the same standard you would for any consequential change: determine whether the submitted code does what it is supposed to do without breaking what it must preserve. A model’s confidence, the code’s style, and a detector’s guess about authorship are not evidence that the patch is correct.
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1. Establish the intended behavior
Ask the author what should change, what should remain unchanged, and which assumptions shaped the implementation. If they know which parts were generated, rewritten, or manually edited, ask for that context too—but treat it as an explanation, not a substitute for examining the patch. Compare the stated goal and test plan with the actual submitted diff.
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First map the files, components, dependencies, data flows, and behaviors affected. Look for changes with no clear connection to the task, a missing migration or rollback, or tests that do not match the implementation. Then select the files and code paths most relevant to the behavior and inspect them closely.
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JetBrains Research’s 2026 proposed framework draws on a participatory design study with 17 practitioners and a follow-up survey of 43 software professionals. It recommends moving from a high-level view to selective inspection of files and code snippets, rather than relying on a line-by-line read of a large, mixed change. This is a proposed review approach, not controlled proof that a particular workflow reduces defects. Read the framework from JetBrains Research.
3. Spend review time where failure would matter
When the change touches them, prioritize authentication and authorization, data access, input validation, error handling, concurrency, persistence, external calls, and security-sensitive configuration. Check whether dependencies and generated files are expected and whether the implementation follows the project’s conventions. These are practical review priorities, not a universal risk checklist established by the cited study.
4. Verify behavior, not just the diff
Run the relevant tests and inspect what they assert. Check whether they cover the intended behavior, edge cases, and failure conditions; a green test run is useful evidence, not proof that every requirement is met. Add or use appropriate static analysis and security checks when they fit the change.
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An automated reviewer can add another signal, but verify each finding against the code and task. In a December 2025 account of its own review system, OpenAI described balancing detection with signal quality and false alarms, and framed automated review as a complement to other oversight. In that deployment, it reported comments on 36% of pull requests entirely generated by its cloud coding agent, with 46% of those comments leading to a code change. Across comments from the deployed reviewer, authors addressed findings with code changes in 52.7% of cases. These are OpenAI’s observations in its own system and codebase context, not independent benchmark results. Read OpenAI’s account of code verification.
What if the code changed after the AI generated it?
Use the final submitted diff as the source of truth. An earlier model draft may help explain how the change began, but only if it is available and clearly connected to the submitted version. A draft does not show which edits survived, and a reviewer cannot reconstruct intermediate outputs that were never recorded.
When a change in authorship or editing history matters, compare available commits, branches, pull-request revisions, or approved audit logs. Ask the author to identify material edits and explain why they were made. If those records do not exist, say what is known from the final patch; do not infer a complete history from coding style.
Can you tell if code was written by AI?
Not reliably from style alone. Patterns that seem “AI-like” can also appear in human-written code, and model-assisted work may be substantially edited. A classifier’s label is not a record of who produced or changed a particular line.
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That distinction matters when interpreting the available figures. In a 2026 Harris Poll for GitLab, 43% of 1,528 developers and technology buyers surveyed across six countries said they could not reliably distinguish AI-generated code from human-written code in their codebase. The same survey found that 85% agreed AI had shifted the bottleneck from writing code to reviewing and validating it. Those are respondents’ reported views, not measurements of code quality or universal outcomes. Read GitLab’s 2026 survey announcement.
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A 2023 study by Bukhari, Tan, and De Carli reported up to 92% accuracy in an ideal-condition evaluation of code-origin classification. That result applies to the study’s selected, cleanly labeled dataset and controlled conditions; it does not establish field-ready accuracy on arbitrary production code or prove who wrote a particular patch. Read the study.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Should you track AI involvement?
When accountability or later incident analysis requires it, record the tool or agent, task or intent, responsible human owner, and material follow-up edits in the pull request or an approved audit trail. Choose a mechanism that fits team policy and repository tooling. Provenance can explain context and responsibility; it cannot replace review of the final code.
GitLab’s accountability framing focuses on where code came from, what it was meant to do, and who remains responsible after deployment. A 2023 study by Bukhari, Tan, and De Carli likewise describes model-based code generation as a software supply-chain inclusion path and motivates provenance tracking. Neither point means every team needs the same logging mechanism. GitLab’s announcement and the study provide further context.
Why a careful review still matters
Automation can speed code production without making validation effortless. The Harris Poll for GitLab reported that 85% of surveyed respondents agreed AI had shifted the bottleneck toward review and validation. These are self-reported perceptions, not a universal measure of review time or proof that AI-generated code is inherently worse.
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