A pull request (PR) review is efficient when it catches real defects, produces feedback the author can act on, gets a reviewer’s first response quickly, keeps the author’s follow-up work small, and lets the PR close soon after it opens. Lines changed and comments per review measure none of those things directly. AI review tools can help with some of them and hurt others, and the published evidence does not show a universal effect in either direction.
How can we make pull request reviews faster without sacrificing code quality?
Start by treating review as a team process with several jobs at once. Google’s 2018 case study of modern code review, which analyzed 9 million reviewed changes alongside 12 interviews and a survey of 44 respondents, describes review as a tool-based practice that serves quality, knowledge sharing, and coordination. A process with those functions cannot be judged by how quickly one reviewer clears a diff. Google’s case study is the origin of that framing, and it describes one large organization, so its numbers should not be read as a benchmark for other teams.
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The practical question is therefore which trade-off you are willing to accept. Speeding up the first reviewer response while letting low-value comments multiply can slow the PR down overall. Shrinking a change while splitting it into many dependent PRs can move waiting time from review into merge coordination. The measures below are the ones that expose these effects.
The five measures that define review efficiency
- Useful defect detection: defects found in review that would otherwise have reached a later stage, judged by the team rather than by a comment count.
- Actionable feedback rate: the share of comments the author accepts, resolves, or judges correct and relevant.
- Reviewer time to first meaningful response: elapsed time from review request to the first comment or decision that moves the PR forward.
- Author follow-up effort: active time the author spends responding to comments, plus the number of review rounds.
- End-to-end PR closure time: elapsed time from opening the PR to merging or closing it.
Only the first measure concerns quality directly, and only the last captures the whole workflow. Track them together, or a gain on one will hide a loss on another.
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Where the time actually goes after a reviewer comments
Review feedback creates work for the author, and that work is easy to undercount. In a 2023 Google Research Blog post on resolving review comments with machine learning, Google reports an average of about 60 minutes of active author shepherding time between sending a change for review and finally submitting it. The same post says that active work grows almost linearly with comment count: “In our measurements, the required active work time that the code author must do to address reviewer comments grows almost linearly with the number of comments.” Those figures come from Google’s internal tooling and workload, and they describe the author’s cost, not the reviewer’s.
The implication for PR design is that every comment carries a cost on the author’s side, whether or not it is correct. A comment that is wrong, duplicated, or about a style point the linter already enforces adds follow-up work without adding safety. This is why comment volume is a poor target: a reviewer who leaves fewer, better-grounded comments may reduce total closure time even though the review looks thinner.
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Do AI code reviews actually save time?
Sometimes, in some settings, and the reported results disagree. The table below lists the studies that bear most directly on the question. The columns are not directly comparable: the populations, tools, tasks, and definitions differ, and two of the studies come from the vendors whose products were tested.
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| GitHub study of Copilot Chat (2023) | Vendor-run study of reviews with Copilot Chat | Reviews reported 15% faster | Vendor-reported and bounded to that study |
| Automated Code Review in Practice (preprint submitted 2024; ICSE 2025 SEIP) | 238 practitioners across ten projects had access to the AI review tool; three projects analyzed, with 4,335 PRs, including 1,568 with automated reviews | 73.8% of automated comments resolved; average PR closure duration rose from 5 hours 52 minutes to 8 hours 20 minutes | Variation across projects; independent industrial deployment |
| Does AI Code Review Lead to Code Changes? (2025 preprint) | More than 22,000 AI review comments in 178 public repositories, covering 16 review actions | Concise, contextual comments with code snippets and manual triggers were more likely to lead to code changes | Preprint; analyzes public repository workflows |
The closure-time result is the one to take seriously
The industrial study of Qodo PR Agent is the most useful counterweight to vendor claims. Its authors reported that automated comments were frequently acted on, yet the average time for a PR to close went up after the tool was introduced. Both results can be true at once: the tool produced feedback people used, and that feedback added steps, waiting, or rework that lengthened the average PR. The increase also varied from project to project, so an average for one team is not a forecast for another.
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A 15% reduction in review time in a vendor study and a longer closure time in an independent deployment measure different things. The first looks at a review task inside a controlled setup. The second looks at the full path from opening a PR to closing it, across real projects. A tool can make one step faster and still lengthen the overall path.
Automated comments can also be resolved and still cost time
A high resolution rate does not prove that the comments were worth the author’s time. The 73.8% figure counts comments that were resolved, which includes comments the author fixed, dismissed, or closed after checking. Pair it with a separate noise measure, such as the share of comments judged irrelevant or wrong, before treating resolution as a success signal.
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More comments are not better comments
The 2025 preprint on GitHub Actions review workflows is useful because it looks at what happens after a comment is posted. Across more than 22,000 AI review comments in 178 repositories, the authors found that concise, contextual comments that included code snippets, and that were triggered manually, were more likely to lead to code changes. Volume alone did not predict action.
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For a team choosing a tool or setting one up, the implication is to look at trigger behavior and comment form, not just the number of findings. A tool that comments on every push produces more text for the author to read. A tool triggered on request, with a specific snippet and a short explanation, gives the author less to triage and a clearer reason to act. This is a finding about one preprint’s data, so confirm it against your own comment logs before relying on it.
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Does smaller code volume mean better review?
Not by itself. GitHub’s 2024 write-up of a controlled study of Copilot’s effect on code quality recruited 243 developers, analyzed 202 valid coding submissions, and collected 1,293 subsequent blind code reviews. Its Copilot group showed fewer code errors per line, and the average commit size was slightly smaller, even though that group made more commits and changed more lines overall. The study shows that volume and quality can move independently in a bounded exercise. It does not show that AI assistance produces smaller PRs in production, and it is not a measure of how real teams review real changes.
The same caution applies to any ideal PR size. The sources in this review do not establish a line-count threshold that improves review outcomes across teams, so treat any fixed limit as a local policy to test rather than a rule.
How to evaluate a review process change
- Record a baseline before changing anything. Pull PR open time, first reviewer response time, number of review rounds, and closure time for at least one recent period. Note the change type and project for each PR.
- Count comments in two groups. Mark each as actionable (accepted, resolved, or judged correct) or noise (wrong, irrelevant, duplicated, or unnecessary).
- Estimate author follow-up effort. Use time logs or a short survey after each review round. A count of rounds alone misses long, shallow rounds.
- Enable the change for a defined set of projects. Compare them against comparable projects that did not change, and stratify results by project and change type.
- Judge the result on closure time and defect outcomes together. A drop in first-response time is only a gain if closure time and escaped defects do not worsen.
When comparing AI review tools or practices, use the same five axes for each: actionability and correctness of comments, review granularity and context, the human effort the tool adds or removes, integration and trigger behavior, and total PR closure time. Keep vendor-reported controlled results separate from independent deployment data when you summarize them.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsLimits of the current evidence
The studies cited here cover different periods, from Google’s 2018 case study through preprints and reports published in 2025, and this article is written in October 2026. Tools and models change quickly, so newer releases may behave differently from the versions studied. Google’s reports describe internal tooling and scale. GitHub’s reported 15% result comes from its own product research. The Qodo PR Agent study shows both higher comment resolution and longer closure time. The 2025 GitHub Actions analysis is a preprint about public repositories. Together, these sources support careful measurement and comparison. They do not support a general claim that AI review makes pull requests faster or slower.
For a team’s own answer, the most reliable evidence will come from its own baseline and its own paired measures.
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