AI can help inspect a pull request, but it does not automatically make a team’s review queue move faster. To find out what is slowing delivery, measure how long a PR waits for its first human review separately from the time reviewers spend reading it and the total time until closure. Then test workflow changes against that baseline.
Does AI code review make pull requests faster?
There is no reliable basis for assuming it does. One industrial case study found that average pull request closure duration increased after an automated review tool was introduced, even though many automated comments were resolved and practitioners reported some quality benefits. The study measured closure duration, not how much time PRs spent waiting versus being actively reviewed, and results varied across projects.
As an Amazon Associate I earn from qualifying purchases.
What the industrial study found
The 2024 study Automated Code Review In Practice examined a software environment where about 238 practitioners across ten projects had access to an automated review tool. Its analysis focused on three projects and 4,335 pull requests, of which 1,568 received automated reviews. The authors reported that 73.8% of automated comments were resolved. Average PR closure duration rose from 5 hours 52 minutes to 8 hours 20 minutes; trends differed among the projects.
Recommended Free Tools
Comment resolution does not prove that a comment was correct or useful. The study also reports faulty reviews, unnecessary corrections, and irrelevant comments. Its findings are specific to the studied environment and do not establish that the tool caused longer closure times, or that AI review will have the same effect elsewhere.
What other evidence can—and cannot—tell you
- DORA’s 2025 report, based on nearly 5,000 technology professionals worldwide and more than 100 hours of qualitative data, describes AI as an amplifier of an organization’s existing strengths and weaknesses. It does not measure PR waiting time.
- GitHub Research’s controlled study reported that submissions created with coding assistance were 5% more likely to be approved in a controlled web-server coding task. That is not a real-world estimate of review-queue speed.
- GitHub’s ReviewBench evaluates AI reviewers against human-reviewed reference findings, including whether they identify useful issues and avoid false positives. Benchmark quality is not evidence that a team’s PRs close faster.
- A 2026 vision paper argues that faster code production can increase the volume of code requiring review and proposes human quality gates for agentic review workflows. It is a design proposal, not an outcome study.
Separate waiting, reviewing, and closure time
A single PR-cycle metric can hide different bottlenecks. Track these clocks independently; this is a practical measurement approach, not a published finding that one of them is usually dominant.
- Time to first human review: elapsed time from opening a PR to its first substantive human review. Define whether an automated comment counts separately; it should not substitute for a human response in this measure.
- Active review effort: reviewer time spent examining the change and providing feedback. If you cannot measure active time directly, do not treat elapsed time between comments as if it were reading time.
- Total time to closure: elapsed time from opening the PR until it is closed or merged. Specify which outcome you report and apply the same definition throughout.
Also track PR size, test readiness, rework, and the frequency of false-positive or unnecessary AI suggestions. These help explain whether a change shortened the queue at the cost of more correction work, or reduced review effort without changing the delivery timeline.
Establish a baseline before changing the workflow
Use a consistent period and comparable groups of PRs. Record the three time measures above, along with change size and review outcomes. Segment results where it matters—for example, by project or type of change—so a shift in PR mix is not mistaken for an improvement.
Free tools Windows power users keep installed
One-click scans. No signup required.
DORA’s 2024 guidance emphasizes fundamentals such as small batch sizes and robust testing, and recommends continuous improvement: assess a baseline, form a hypothesis, then measure iteratively. The specific PR metrics here are a way to apply that approach, not metrics DORA publishes as a required set.
Rank #3
Test changes that target the bottleneck you measured
Do not introduce AI review as a stand-in for diagnosing the delay. Choose a small intervention that addresses the observed constraint, then compare results with the same measures and definitions you used for the baseline.
If PRs wait too long for a first human response
- Try clearer reviewer routing or ownership so a request reaches someone able to act on it.
- Check whether reviewer capacity or competing work is leaving requests unattended before adding more automated comments.
- Use an AI first pass only as an experiment. Evaluate whether it helps reviewers respond sooner without adding distracting findings or avoidable corrections.
If active review effort is high
- Reduce change size through smaller batches, making each PR easier to understand and assess.
- Improve test readiness so reviewers can evaluate a change with relevant automated checks available.
- Assess AI findings for usefulness and false positives. A high comment-resolution rate alone is not a quality measure.
If closure time remains high
Look at the full path to closure, including author revisions and re-review, rather than assuming the initial human review is responsible. Compare whether a change affects first-response time, active review effort, rework, and total closure time; those outcomes can move in different directions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep human accountability and knowledge sharing
AI can suggest issues, but a human reviewer remains responsible for judging whether a change is safe, correct, and suitable for the project. Evaluate any automated reviewer both for useful detection and for the cost of false alarms or unnecessary edits. Also watch whether the workflow preserves the human discussion and shared understanding that code review can provide.
Quick wins for a faster PC:
Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →There is no published estimate in the cited evidence of what share of PR review time is spent waiting rather than reading. The practical case for measuring the distinction is precisely that closure time alone cannot answer it.
Quick Recap
Best Value
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
