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A review that cannot identify the exact pull requests, revisions, and reproduced defects cannot honestly claim to have reviewed three AI-written PRs. The available evidence supports a practical review standard—not first-hand findings about three specific changes. For any PR, block the merge when you can show that it violates the requested behavior, creates a material security risk, or breaks a repository contract; distinguish those defects from missing tests and non-blocking style preferences.
What evidence is needed to call a PR a blocker?
A merge-blocking comment should connect a concrete defect to evidence and impact. Record the PR URL and commit SHA, the behavior the change promises, the relevant code or project contract, and a reproducible test, failure, or security finding. Without that information, a claim about a particular PR is not verifiable.
- Block: the change demonstrably fails its intended behavior, breaks a supported use case, violates a security boundary, or contradicts a required repository contract.
- Request changes or more evidence: the change may be correct, but a material edge case is untested or the author has not established that a risk is addressed.
- Suggest, but do not block: a preference about naming, formatting, or an alternative implementation that does not materially affect correctness, security, or maintainability.
GitHub’s guidance recommends reviewing functionality, security, and maintainability, and using checklists to cover those areas: Review AI-generated code. The same criteria apply whether a person or a model wrote the patch.
How to review an AI-written PR
- Pin down the change. Read the issue or request, PR description, diff, and repository context. Identify what should change and what must remain compatible.
- Trace the behavior. Follow relevant callers, data flows, dependencies, permissions, and error paths. Check ordinary inputs and meaningful boundary cases rather than judging the diff by how plausible it looks.
- Inspect security boundaries. Look for unsafe input handling, authorization mistakes, exposed secrets, unintended data access, and failure behavior. A security concern should be described in terms of the affected asset, conditions needed to exploit it, and likely impact.
- Check the tests against the changed behavior. Determine whether tests would fail if the defect were present. Run the project’s documented test and lint commands when available; passing checks do not establish correctness if the changed case is not covered.
- Use automation as supporting evidence. Review CI results and applicable dependency or security scans. GitHub documents that AI-related security and quality features have scope and coverage limits, so a clean scan is not proof that a patch is safe: Responsible use of GitHub Copilot code review.
- Make the decision explicit. For each blocking comment, state the observed failure, evidence, and impact. Ask for a correction or a test that resolves the specific concern; do not use “AI-written” as the reason.
What published evidence says—and does not say
One 2024 study analyzed 452 Copilot-generated snippets found in GitHub projects and reported security weaknesses in 134, or 29.6% of that sample. The reported figures were 91 of 277 Python snippets (32.8%) and 43 of 175 JavaScript snippets (24.6%), spanning 38 CWE categories. Those results concern identified snippets in two languages and a particular tool and study context; they are not a failure rate for AI-written pull requests. See Fu et al., Security Weaknesses of Copilot Generated Code in GitHub.
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ReviewBench offers a separate source of real-world review cases: its public benchmark describes 219 pull requests from 187 open-source licensed repositories across 19 languages, with human-reviewed reference findings. GitHub says the benchmark was developed from analysis of 103.9 million pull requests; the benchmark’s selection is weighted toward changes considered reviewable rather than being a direct mirror of all PR sizes. These figures describe benchmark construction, not the prevalence of AI-written PRs or their defect rate. See the ReviewBench repository and GitHub’s ReviewBench article.
A 2024 study of 18,256 pull requests containing AI-crafted description content found less review time and a higher likelihood of merging; developers also often edited the generated descriptions. That research concerns PR descriptions and process outcomes, not proof that the code in those PRs was better or safer. See Xiao et al., Generative AI for Pull Request Descriptions: Adoption, Impact, and Developer Interventions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why the final merge decision stays with a person
A benchmark can help evaluate review systems, and automation can surface issues, but neither substitutes for a reviewer who understands the change’s context and owns the decision. GitHub’s stated position is: “At GitHub, we think the answer hasn’t fundamentally changed: it’s the developer who hits ‘Merge.’” Its blog describes the PR as “the audit log, the governance layer, and the social contract that says nothing ships until a person is willing to own it.” These are GitHub’s views on accountability, not independent empirical findings. See Code review in the age of AI and Agent pull requests are everywhere. Here’s how to review them.
Because no three PR URLs, revisions, or verified findings are established here, there is no sound basis to say which specific changes should have been blocked. A defensible three-PR review needs those primary examples and evidence for each observed defect; the standards above show how to reach and explain that judgment.
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