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AI code review can put another set of eyes on a pull request, but a count of 100 reviews alone cannot show whether it caught bugs or improved delivery. GitHub Copilot, for example, can review new pull requests automatically, yet its default review is a comment—not an approval—and later pushes need a separate automatic re-review setting. Without a documented tool, review period, and outcome data, there is no substantiated account of what happened after a particular 100th review. Here is what the workflow can do, what the available evidence supports, and how to judge an experiment responsibly.
What does “AI reviewed every PR” actually mean?
Coverage depends on what counted as a pull request (PR) and when the review ran. Reviewing every newly opened PR is not the same as reviewing every subsequent push. Nor does a tool configured in one repository necessarily cover an organization’s other repositories.
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To make a 100-review result interpretable, an account should identify the actual tool, repositories included, date range, review setting, and whether the count includes first reviews only or re-reviews after updates. It should also explain how it counted duplicate or ignored comments and what qualified as a useful finding. The evidence available here documents GitHub Copilot’s capabilities and published studies; it does not establish an author’s own 100-review experiment or its results.
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GitHub documents requesting a Copilot review from the GitHub interface or through its REST API, as well as configuring automatic reviews for new pull requests. GitHub says a review usually takes less than 30 seconds; that is a typical documented duration, not a guaranteed response time. The review can label comments by severity and may suggest changes. See GitHub’s instructions for using Copilot code review.
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New pull requests and later pushes are separate
Automatic reviews can be enabled for new PRs. To have Copilot automatically review later pushes to an open PR, select the new-push re-review setting as well. Without that option, a change made after the first review does not automatically trigger another one. That distinction matters when measuring coverage: an opening review may not reflect the code that is eventually merged.
GitHub also warns that a re-review may repeat comments that a person has resolved or downvoted. Repeated findings therefore need to be distinguished from new findings when counting useful comments.
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A comment is not an approval
GitHub Docs says: “By default, Copilot leaves a "Comment" review, not an "Approve" review or a "Request changes" review.” Its default review does not satisfy required approvals or act as a merge gate. Submitting approval reviews is an optional public-preview feature, according to the documentation accessed October 7, 2026. Teams should not treat an AI review’s assessment as a substitute for their configured approval rules.
Review effort affects cost
GitHub describes Lite effort as targeted feedback on glaring issues, including bugs, security vulnerabilities, and style inconsistencies. Balanced effort uses higher reasoning for complex logic, security-sensitive code, and changes spanning services. GitHub’s undated documentation accessed in 2026 estimates AI-credit consumption at $0.05–$1 per Lite review and $0.25–$5 per Balanced review. These are estimates, not fixed prices: usage generally increases with PR size and custom instructions, and estimates can change as models evolve. They exclude GitHub Actions minutes. A team’s total depends on its PR volume and effort choices; the published ranges alone do not establish a monthly bill. Details are in GitHub’s overview of Copilot code review.
What the published evidence says—and does not say
Usage is not proof of quality
On March 5, 2026, GitHub reported that Copilot had completed 60 million code reviews. That is a company-reported usage figure, not independent evidence that reviews are accurate, that developers act on them, or that software quality improves. See GitHub’s announcement.
A security evaluation found important misses in its test cases
A 2025 preprint by Amena Amro and Manar H. Alalfi tested Copilot code review against intentionally vulnerable datasets. The authors report examples in which many files were reviewed but relevant vulnerabilities were not identified; in one Wireshark test suite, they report 878 of 898 files reviewed with zero comments. This is a specific controlled evaluation, not proof that every AI review misses security flaws or performs poorly across repositories, model versions, and settings. It does show why a review tool should not be treated as a security guarantee. Read the study and its stated evaluation scope.
A student study measured follow-up commits, not correctness
A 2026 experience report on AI-assisted code review in software-engineering education found that 32% of successfully AI-reviewed PRs in its 2023 cohort and 33% in its 2024 cohort were followed by subsequent commits on the same PR. The authors also report that 93% of teams in the 2023 cohort and 50% in the 2024 cohort used the tool. These results describe student cohorts, not professional teams; a later commit does not establish that an AI comment was correct, accepted, or caused the change. See the 2026 experience report.
How to evaluate a 100-review trial
A review count is a measure of activity. To assess usefulness, report outcomes against a defined denominator and separate kinds of findings rather than treating comment volume as success.
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- Define the sample: name the repositories, date range, included PRs, and whether the trial covered opening reviews, later pushes, or both.
- Describe the configuration: identify the tool and effort setting, how automatic review was enabled, and whether repository instructions or other settings changed during the trial.
- Classify findings: count actionable findings, false positives, accepted suggestions, and duplicate or repeated comments separately. Explain the criteria used for each label.
- Track independent checks: record issues later found by human reviewers or tests, without assuming every such issue was detectable from the AI’s review context.
- Measure workflow effects cautiously: report timing and workload measures with their definitions. A change in merge time or comment count by itself does not show that the AI caused an improvement.
- Keep security claims bounded: report the types of security issues tested and how they were validated. A clean AI review is not evidence that a PR is vulnerability-free.
If comparing periods before and after adoption, use comparable measurement methods and workloads. Otherwise, a difference may reflect changes in PR size, team practices, testing, or other factors rather than the reviewer.
What to check before enabling automatic reviews
- Confirm which repositories and newly opened PRs are included; do not assume later pushes are reviewed unless the re-review setting is enabled.
- Decide how developers should handle severity labels, suggested changes, repeated comments, and findings that appear incorrect.
- Keep required human approvals and other merge protections in place; the default Copilot comment review does not satisfy approval requirements.
- Estimate AI-credit use from expected PR volume, review effort, PR size, and repository instructions, and account separately for Actions minutes.
- Evaluate false positives and security misses as well as useful findings. Review automation is an additional signal, not a replacement for tests, security checks, or accountable human review.
GitHub’s July 18, 2025 changelog notice also documents planned deprecations and changes to Copilot code review. Because product behavior and availability can change, check the current documentation and changelog when configuring a live workflow.
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