Free tools Windows power users keep installed
One-click scans. No signup required.
iTechGuides is reader-supported. When you buy through links on our site, we may earn an affiliate commission. As an Amazon Associate I earn from qualifying purchases. Learn more
To shorten pull request (PR) review time, find where work waits across preparation, assignment, review, revisions, automated checks, approvals, and merge. Asking reviewers to respond faster will not fix a queue caused by unclear ownership, oversized changes, slow checks, or a cross-time-zone handoff. Measure those stages first, then improve the constraint without sacrificing review quality or developer focus.
What does “PR review time” measure?
There is no single clock that captures the whole review process. Time to first human response, time spent actively reviewing, time between review rounds, time waiting for checks, and elapsed time from review-ready to merge describe different delays. Google Engineering Practices explicitly distinguishes response speed from the time it takes a change to complete review and be submitted. Google’s guidance on review speed is a useful distinction, not a universal service-level agreement.
Start by naming the interval you want to improve. If authors wait most of a day for an initial response, a faster CI runner will not address that delay. If reviewers respond quickly but changes sit through repeated revisions or pending checks, a first-response target alone will not shorten time to merge.
Quick wins for a faster PC:
Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Map the workflow before changing it
Instrument timestamps for the stages that matter in your repository: marked ready for review, reviewer assigned, first human response, changes requested, author update, required checks completed, approval, and merge. Use these events to distinguish queue time from active work and identify repeated handoffs.
#1 Best Overall
Segment the measurements
Compare like with like. Break results down by change size, team or ownership boundary, working-hour overlap, and risk. A small change reviewed by a nearby teammate is not comparable to a large change that crosses team boundaries and waits overnight for a specialist. Averages can hide those differences; examine the distribution and the delayed cases as well.
Keep speed tied to outcomes
DORA recommends looking at the delivery process end to end, including lead time and change-failure indicators, rather than treating approval speed as the only result. Track first-response and total merge time alongside rework and failed changes so a faster queue does not come at the cost of missed risks or avoidable corrections. DORA’s guidance on streamlining change approval frames peer review and automation as complementary parts of the workflow.
Reduce waiting without demanding constant interruption
Make review ownership and escalation clear
Route each change to a qualified, available reviewer, and make ownership and backup paths visible. If the ideal reviewer is unavailable, Google’s guidance suggests finding another suitable reviewer rather than letting the request remain stuck. For teams spanning time zones, make the handoff explicit and time responses so the author can act during their working day.
Set a response norm people can sustain
Google Engineering Practices says, “One business day is the maximum time it should take to respond to a code review request (i.e., first thing the next morning).” That is Google’s stated practice, not a universal SLA. The same guidance advises reviewers not to interrupt focused work and to respond at reasonable break points. Teams should document a norm that fits their work hours, urgency, and coverage rather than reward continuous monitoring.
Make requests reviewable
Split an oversized change into smaller dependent changes when practical. Smaller batches let reviewers inspect and respond sooner, but dependencies need clear order and ownership; a chain that leaves the next change blocked can simply move the wait elsewhere. When a change cannot be split safely, seek early high-level design feedback so the author can resolve major concerns before polishing implementation details. Google’s review guidance recommends smaller changes while keeping code health in view.
Move repeatable feedback earlier, keep human judgment where it matters
Run reliable, repeatable validations during development and through continuous integration (CI): tests, security controls, and other checks that can detect problems before a reviewer has to find them manually. DORA recommends peer review during development, supplemented by continuous testing, CI, monitoring, and observability for fast feedback. Riskier changes may warrant extra scrutiny; automation should not replace human attention to design, behavior, maintainability, and context-sensitive risk.
Evaluate an automation change by its effect on the whole flow, not by whether it was adopted. A study of 5,000 repositories found that 1,489—almost 30% of the sample—adopted GitHub Actions; the researchers also reported more time to accept a PR after adoption. That repository-level association does not establish that Actions caused longer acceptance times in every project, but it is a reason not to assume that adding automation automatically shortens review. Wessel and co-authors’ study reports the finding.
Use automation carefully, including AI-assisted review work
Google Research described an internal deployment that applied machine-learning-suggested edits to reviewer comments. In that Google setting, authors applied a suggested edit to 7.5% of all reviewer comments. Google estimated the system could save hundreds of thousands of engineer hours annually at its scale. Neither figure is a general benchmark, and the 7.5% is not a measure of total review time saved. The paper describes Google’s system and results.
The broader lesson is to test automation against a specific bottleneck and preserve the quality signals that matter. DORA’s 2025 report describes research involving more than 100 hours of qualitative data and nearly 5,000 technology professionals; its recommendations can inform local hypotheses, but the workflow still needs measurement in your own organization. The 2025 DORA report provides that context.
Rank #4
Run a focused experiment, then reassess the constraint
- Establish a baseline. Record first-response time, time between review rounds, time waiting on checks, total time to merge, rework, and failed changes. Segment the results by change size, handoff pattern, and working-hour overlap.
- Choose one bottleneck. For example, clarify reviewer routing if requests sit unassigned, or improve CI feedback if changes wait on checks. Avoid changing several stages at once if you want to know what affected the result.
- Define the guardrails. Decide what quality and focus signals must remain healthy, such as rework, failed changes, or reviewer interruption. Do not treat a shorter interval as a win if it shifts hidden costs to another stage.
- Compare after the change. Check the same measures against the baseline. Tool adoption alone does not show that an intervention helped; look for changes in both the targeted wait and end-to-end outcomes.
- Repeat where the evidence points. If the queue moves—for example, from assignment to slow checks—address the new constraint rather than pressuring the same reviewers to work faster.
For any workflow or platform option, assess review routing, queue visibility, support for small batches, CI feedback latency, risk-based checks, cross-time-zone handoffs, and the quality signals retained. These are evaluation criteria, not a product ranking.
Why there is no universal ideal review time
Review latency depends on the change, its risk, team ownership, working hours, and the behavior of checks and handoffs. Google’s recommendations are practitioner guidance from one organization, while DORA offers a process model to test locally. A 2026 first-person account by Google Cloud’s Lee Boonstra describes AI-generated code volume shifting a team’s bottleneck toward review and integration, with dependencies and merge conflicts contributing to gridlock; it is an illustration of one team’s experience, not a comparative study. Boonstra’s account shows why increasing code output can expose a different constraint.
There is no supported universal target in this evidence for ideal PR review time. Define the clock, find the waiting point, and judge changes by delivery speed together with quality and sustainable reviewer focus.
For background on DORA’s research program, see DORA Research.
Quick Recap
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.
