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To make pull request reviews faster without sacrificing code quality, optimize the whole review loop—not just lines changed or comments posted. Track whether feedback catches real issues, how quickly reviewers respond, how much follow-up authors must do, and how long a pull request takes to close. AI review can help in some settings, but published findings do not show that it reliably shortens review time across teams.
What makes a pull request review efficient?
An efficient review reaches a sound decision with useful feedback and reasonable effort from both reviewer and author. Review also supports knowledge-sharing and coordination, so judging it only by code volume, comment count, or speed can miss important outcomes.
Google’s 2018 case study examined 9 million reviewed changes, alongside 12 interviews and a survey of 44 respondents. It describes modern code review as a tool-based team practice, not simply a line-by-line defect hunt. The study reflects one large organization, so its findings should not be treated as a universal benchmark. Google Research’s case study
Review efficiency is best understood as a balance among:
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- Defect detection: Does review identify meaningful correctness, security, or maintainability problems?
- Actionable feedback: Can the author understand and address comments without unnecessary back-and-forth?
- Reviewer effort and response: How long does it take to provide a meaningful first response, and how much reviewer time does the change require?
- Author effort: How much active work is needed to respond and submit a final revision?
- End-to-end duration: How long does the pull request remain open before closure?
How can you make pull request reviews faster without sacrificing code quality?
Use a small set of paired measures so that an apparent speed improvement does not conceal more rework, missed defects, or low-value feedback. Avoid adopting an “ideal” pull request size threshold as a universal rule: the evidence here does not establish one.
Measure both speed and usefulness
For each team or project, track the following together, using consistent definitions:
- Time to first meaningful reviewer response and reviewer time spent.
- Author active follow-up time and number of review rounds.
- Share of comments accepted, resolved, or judged actionable.
- Noise, including false positives, irrelevant comments, and unnecessary corrections.
- Total pull request closure time.
Compare results by project, change type, and whether AI review was enabled. That helps distinguish a tool’s effect from differences in the work being reviewed.
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Improve the feedback loop, not the comment count
More comments do not necessarily mean a better review. A 2025 preprint examined more than 22,000 AI review comments across 178 repositories and 16 review actions. Its findings indicate that concise, contextual comments with code snippets and manual triggers were more likely to lead to code changes. The study concerns public repository workflows, and its results should not be generalized to every team or review setup. Read the preprint, “Does AI Code Review Lead to Code Changes?”
When adjusting a review process or tool, examine whether comments are correct and actionable, whether they have enough code context, how much human effort they add or remove, how and when the tool is triggered, and what happens to total closure time. A high resolution rate alone does not show that comments were valuable: authors may resolve comments that are unnecessary, while useful feedback can require discussion rather than a direct change.
Do AI code reviews actually save time?
Sometimes, but the answer depends on the tool, team, task, and what “time saved” measures. A faster review interaction does not necessarily mean less author work or a shorter end-to-end pull request.
| Evidence | Reported result | What it can—and cannot—tell you |
|---|---|---|
| GitHub’s 2023 Copilot Chat study | GitHub reported code reviews were 15% faster in its study. | This is a vendor-reported result bounded to that study; it is not a guarantee for other tools or teams. GitHub’s study |
| Industrial study of Qodo PR Agent, reported at ICSE 2025 SEIP | 238 practitioners across ten projects had access to the tool. The analysis covered three projects and 4,335 pull requests, including 1,568 with automated reviews. The authors reported that 73.8% of automated comments were resolved, while average closure duration increased from 5 hours 52 minutes to 8 hours 20 minutes, with variation across projects. | Comment resolution and closure time moved in different directions in this deployment. The study’s project-level variation matters; it does not prove that AI review generally lengthens closure time. Study record and paper details |
| Google’s internal comment-resolution work | Google reported about 60 minutes of average active author shepherding time between submitting changes for review and final submission. It also reported that author effort grew almost linearly with comment count. | This is a Google-specific finding about active author work, not a general estimate of pull request duration or a target for other teams. Google Research’s report |
These findings are not directly comparable: they involve different organizations, tools, populations, tasks, and definitions of time. GitHub’s 15% figure concerns reviews in its study; the industrial study reports overall pull request closure duration; Google reports active author shepherding time.
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A separate GitHub controlled study recruited 243 developers; 202 produced valid coding submissions, followed by 1,293 blind code reviews. GitHub reported fewer code errors per line in the Copilot group and a slightly smaller average commit size, despite more commits and lines changed overall. This bounded exercise shows that code volume and quality can move independently; it does not establish that AI always improves production code review or makes pull requests smaller. GitHub’s 2024 study
Best Value
How should a team evaluate an AI review tool?
Run the comparison on your own workflow and look at paired outcomes, rather than selecting a tool based on a single speed claim or comment-resolution percentage.
- Establish a baseline. Record reviewer response and effort, author follow-up time, review rounds, comment actionability, noise, and closure time before changing the process.
- Compare like with like. Separate results by project and change type, and distinguish pull requests with AI review from those without it. Avoid treating results from unrelated studies as if they shared a common benchmark.
- Inspect comment quality. Sample comments for correctness, relevance, context, and whether they lead to a justified change. Track unnecessary corrections as noise.
- Account for human effort. Check whether reduced reviewer effort is offset by extra author work, or whether faster feedback changes the number of review rounds.
- Judge the whole workflow. Assess whether useful defect detection and actionable feedback improve without an unacceptable increase in author effort or total closure duration.
A tool may be worthwhile even if it does not shorten every pull request—for example, if it surfaces important issues earlier without imposing substantial noise. Conversely, a high resolved-comment rate is not sufficient evidence of success if closure takes longer or authors spend more time handling low-value feedback. The appropriate trade-off depends on the team’s quality and delivery needs.
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