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A model that generated code can help spot problems in it, but its approval is not independent evidence that the code is correct. Use self-review as an extra pass: the person responsible for the change should understand the diff, run relevant checks, and retain responsibility for deciding whether it is ready to merge.
What a self-review can—and cannot—tell you
A model can identify defects in code it generated, and automated review may surface issues worth investigating. But the same model may carry assumptions from generation into review, and a clean result does not establish that the implementation meets its requirements.
OpenAI’s December 2025 report on a deployed code reviewer found that review performance declined more quickly as inference budget fell on model-generated code than on human-written code. The authors also say their evaluation set consists of issues already identified by humans, so it cannot establish whether additional findings are correct without further human input. They note that the reviewer and generator use the same underlying model for different tasks, and that “there is no clean direct measurement” of the proposed verification advantage. These are observations from one deployment, not a universal rate or independent replication. OpenAI’s report explains the evaluation and its limits.
The Tool Desk
Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →The report includes useful deployment figures, but they should be read in context: 36% of pull requests entirely generated by Codex cloud received a code-review comment, and 46% of comments on those PRs led to an author code change. The corresponding reported figure for comments on human-generated PRs was 53%. In that same deployed workflow, 52.7% of the reviewer’s comments led authors to address a finding with a code change. These numbers describe OpenAI’s system and environment; they are not estimates of how often AI review catches real defects across projects.
#1 Best Overall
What benchmark results say about AI review
A 2025 study tested GPT-4o and Gemini 2.0 Flash on AI-generated code blocks of varying correctness. On 492 blocks accompanied by problem descriptions, GPT-4o classified correctness correctly 68.50% of the time and corrected code 67.83% of the time. Gemini 2.0 Flash scored 63.89% and 54.26%, respectively. These are results for that study’s benchmark tasks, not real-world accuracy rates for pull requests.
The researchers also evaluated 164 canonical HumanEval examples and report that results differed. Performance declined when problem descriptions were omitted; the abstract does not give a single summary percentage for the separate HumanEval set. The study shows why task context matters, but it does not establish how either model performs across production repositories. Read the study’s methods and results.
Which review checks answer which questions?
Review methods are complementary, not interchangeable. The sources available do not provide a controlled head-to-head ranking of every approach, so choose checks based on the evidence they can inspect and the failure modes they can catch.
| Review method | What it can contribute | What it cannot establish by itself |
|---|---|---|
| Self-review by the generating model | A fast additional pass that may flag suspicious code or suggest issues to verify. | Independent approval or proof that requirements are met; the generator and reviewer may share assumptions. |
| Another AI reviewer | A different perspective, especially when given meaningful task and repository context. | Guaranteed independence. The evidence here does not show that changing model or vendor ensures uncorrelated errors. |
| Tests | Evidence about behaviors exercised by the tests and conditions they cover. | Proof of behavior the tests do not exercise, or of every unstated requirement. |
| Static and security checks | Evidence about the patterns, rules, and vulnerabilities those tools are designed and configured to detect. | Complete correctness or coverage of every project-specific risk. |
| Human review | Judgment against requirements, intent, repository context, and likely failure modes. | Automatic detection of every defect; reviewers still need adequate context and time. |
AI findings should be treated as hypotheses: reproduce the issue, compare it with the requirements and surrounding code, and discard false alarms. OpenAI’s report frames review as a trade-off among finding correctness issues, the cost of verification, and the damage caused by false alarms.
A safer workflow for AI-generated changes
- Read the diff yourself. Be able to explain the change’s purpose, assumptions, and likely failure modes before asking others to review it. LLVM’s contributor policy explicitly requires contributors to read and review LLM-generated code or text before requesting project review, and holds the contributor accountable as the author. See the LLVM AI Tool Use Policy.
- Run relevant tests and automated checks. Use the project’s tests, static analysis, and security checks, then interpret each result according to what that check actually covers. A passing test suite is evidence for tested cases, not a certificate for all requirements.
- Get an appropriately informed review. Ask a human reviewer—or an additional reviewer with meaningful task and repository context—to inspect the change. A second AI pass can add perspective, but do not treat model or vendor diversity as proof of independence.
- Verify each finding. Reproduce proposed bugs or vulnerabilities and check whether the behavior violates a requirement. Confirm fixes with the relevant tests or checks rather than accepting a suggested edit on trust.
- Keep merge responsibility with a person. The author or responsible engineer should make the final decision and be able to stand behind the accepted code.
When using an AI code-review service
Automated service output is only as useful as its configuration and coverage. GitHub’s documentation says Copilot code-review approvals do not count toward required approvals by default, although settings can enable that behavior. It also documents file exclusions—including dependency-management files, logs, and SVGs—and plan, policy, and budget controls. Check the current repository settings and applicable plan before relying on it for a particular file or approval rule; these product details can change. GitHub’s documentation describes the service, not comparative evidence that it reviews better than other approaches. Check GitHub’s current Copilot code-review documentation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What recursive-training research does—and does not—show
A 2026 preprint examines repeated fine-tuning in which generated code is fed back into training. It compares model-independent filters, such as compilation and static quality checks, with model self-gating, and reports that independent filters slow but do not prevent degradation in that repeated-training setting. This is not a study of ordinary pull-request review, so it does not show that asking an assistant to review a single patch causes model collapse. Read the preprint and its stated scope.
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