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An AI reviewer can be configured to comment on new pull requests, but “reviewed every PR” does not mean every update was checked, every finding was useful, or a pull request was approved. GitHub Copilot code review supports both requested and automatic reviews; whether it catches bugs reliably depends on what was reviewed and how results are measured.
The available evidence does not establish the outcome of a particular author’s 100-review experiment. Rather than invent results, this article explains what a credible 100-PR test needs to track and what current documentation and published studies can—and cannot—tell you.
What “every PR” needs to mean
A review count is meaningful only with a defined denominator. State which repositories were included, the start and end dates, and whether the count covers every new pull request or only a selected subset. Also say whether updates to existing pull requests were reviewed: automatic review on opening and automatic re-review after later pushes are separate settings.
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Identify the product, configuration, and effort setting used. For each review, record whether it produced comments and classify those comments consistently. A useful experiment distinguishes actionable findings from false positives, repeated comments, style preferences, and suggestions that were not adopted. It should also note defects found independently by people, tests, or other checks.
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Report outcomes, not just activity
- Coverage: number of eligible PRs and number actually reviewed, with exclusions explained.
- Usefulness: actionable findings, false positives, accepted suggestions, and duplicate or ignored comments.
- Independent checks: bugs or security issues found by human reviewers or tests, including those the AI missed.
- Workflow: review timing and any change in PR duration, compared only with a comparable workload and measurement method.
Comment volume alone does not show quality, and a before-and-after change in merge time does not establish that the AI caused it.
What GitHub Copilot code review actually does
GitHub documents requesting Copilot as a reviewer in the GitHub interface or through its REST API, as well as configuring automatic reviews for new pull requests. Its documentation says a review usually takes less than 30 seconds; that is not a guaranteed response time. Copilot labels comments by severity and may suggest changes. See GitHub’s instructions for using Copilot code review.
New pull requests and later pushes are different
Automatic reviews can be enabled for new pull requests. To have Copilot review subsequent pushes automatically, select the setting for re-reviewing new pushes. Without it, an update to an already-open PR does not automatically trigger another review. That distinction matters if an experiment claims coverage of every change rather than just every PR opening.
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GitHub also cautions that a re-review may repeat comments that someone resolved or downvoted. An experiment should count repeated comments separately rather than treating them as new findings.
A comment is not an approval
GitHub says the default Copilot review is a “Comment” review, not an “Approve” or “Request changes” review. It does not satisfy a required human approval for merging. Approval submissions are an optional public-preview feature, not the default behavior. Treat the AI’s assessment as feedback, not as a merge gate or a substitute for the repository’s required review process.
Effort settings and review costs
GitHub describes Lite as targeted feedback on conspicuous issues such as bugs, security vulnerabilities, and style inconsistencies. Balanced uses more reasoning for complex logic, security-sensitive changes, and work that spans services. GitHub’s documentation gives the following estimated AI-credit costs; the documentation does not state the estimates’ publication year and was accessed in 2026.
| Effort | GitHub’s estimated cost per review | Stated use |
|---|---|---|
| Lite | $0.05–$1 | Targeted feedback on glaring issues, including bugs, security vulnerabilities, and style inconsistencies |
| Balanced | $0.25–$5 | Higher reasoning for complex logic, security-sensitive code, and cross-service changes |
These are estimates, not fixed prices. GitHub says usage generally rises with pull-request size and custom instructions, and estimates can change as models evolve. The figures exclude GitHub Actions minutes. A team cannot derive a reliable monthly bill from the ranges alone without its PR volume, chosen effort level, and separate Actions costs. Details are in GitHub’s overview of Copilot code review.
What published evidence can—and cannot—say
Usage is not proof of quality
In a March 5, 2026 blog post, GitHub reported that Copilot had completed 60 million code reviews. That is a company-reported usage figure; it does not measure accuracy, defect detection, or impact on software quality. See GitHub’s announcement.
Best Value
A controlled security evaluation found important misses
A 2025 preprint by Amena Amro and Manar H. Alalfi tested Copilot code review against intentionally vulnerable datasets. The authors report cases where many files were reviewed but relevant vulnerabilities were not identified. In one Wireshark test suite, they report 878 of 898 files reviewed and zero comments. This is a specific controlled evaluation, not evidence that every AI review, repository, model version, or setting will perform the same way. It is a reason not to rely on automated comments as the sole security check. Read the preprint.
Student PR follow-up is not a professional adoption rate
A 2026 software-engineering education experience report found that 32% of successfully AI-reviewed pull requests in its 2023 student cohort and 33% in its 2024 cohort were followed by subsequent commits on the same PR. The paper also reports that 93% of teams in the 2023 cohort and 50% in the 2024 cohort used the tool. Those figures describe student settings; subsequent commits do not establish that AI comments were correct or adopted, and they should not be treated as a rate for professional teams. See the study.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to judge your own 100-review result
After defining the cohort and review configuration, present the results in separate categories rather than collapsing them into a single “caught bugs” count. For every claimed useful finding, explain how usefulness was determined—for example, whether a human confirmed it or a test reproduced the issue. Keep security findings distinct from style suggestions, and distinguish comments from defects that the tool demonstrably detected.
The Tool Desk
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Practical takeaway for teams
Automatic AI review can add a first pass to new PRs, and a separate re-review setting can extend coverage to later pushes. Teams should check that configuration matches what they mean by “every change,” budget for variable review usage, and retain human review and testing—especially for security-sensitive code. A 100-review total becomes persuasive only when its denominator, findings, misses, false positives, and comparison method are visible.
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