To cut pull request review time, find where work is waiting—before asking reviewers to work faster. A PR can stall before assignment, between review rounds, while an author makes revisions, during automated checks, or after approval while it waits to merge. Measure those intervals separately, then improve the slowest handoff without sacrificing review quality or developer focus.
What does “PR review time” measure?
There is no single clock that captures the whole process. Google Engineering Practices distinguishes response time from the time it takes a change to complete review and be submitted. For your team, define the start and end points before comparing results.
As an Amazon Associate I earn from qualifying purchases.
- Time to first response: ready-for-review to the first human response.
- Time between review rounds: a reviewer request or comment to the next author update, and the author update to the next reviewer response.
- Time waiting on checks: when required automated checks start and finish, including delays before they run.
- Total time to merge: ready-for-review to merge.
These measurements expose different constraints. A quick first reply can coexist with a slow merge if revisions, CI, approvals, or handoffs take a long time. Conversely, a long elapsed interval does not mean reviewers spent that entire time actively inspecting code. Google’s review guidance focuses its speed advice on response time, rather than treating end-to-end completion as the same measure.
How to locate the bottleneck
Instrument the handoffs
Capture timestamps for ready-for-review, reviewer assignment, first human response, requested changes, author updates, required-check completion, approval, and merge. Use them to distinguish active work from time in a queue. Segment results by change size, team or ownership boundary, risk, working-hour overlap, and number of handoffs; an overall average can hide a queue affecting only one group or type of change.
#1 Best Overall
Use DORA’s diagnostic questions as a starting point: How long passes between code completion and review? What is the average batch size? How many teams and locations take part? Do review suggestions improve quality automation? DORA recommends looking at the approval process end to end and tracking delivery outcomes such as lead time and change-failure rate, rather than optimizing a single timestamp in isolation. Its guidance is a way to frame local hypotheses, not a guarantee that one target fits every team. DORA’s change-approval guidance describes this approach.
Read the delay pattern before changing the process
- Long wait before the first reply: inspect assignment, reviewer availability, ownership clarity, and working-hour overlap.
- Long gaps between rounds: examine whether review requests, author updates, or follow-up ownership are unclear.
- Checks dominate elapsed time: investigate when checks are triggered, how long they wait to start, and whether repeatable feedback can arrive earlier.
- Slow merge after approval: check integration queues, dependent changes, merge conflicts, and the path from approval to submission.
- Delays rise with change size or handoffs: make batches easier to review and clarify dependencies and ownership.
Improve the flow without disrupting focused work
Route review to an available qualified person
Make ownership and escalation paths visible, route work to a qualified reviewer who can respond, and specify what authors should do if a request is waiting. Google’s guidance says to find another reviewer when the ideal reviewer is unavailable. For teams spread across time zones, plan handoffs so a reviewer’s response arrives while the author can still act during their workday.
Set an explicit response norm that reflects your team’s coverage and work patterns. 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 guidance, not a universal service-level agreement. The same guidance advises reviewers not to interrupt focused work unnecessarily and to respond at reasonable break points. A response norm should reduce uncertainty without turning every notification into an immediate interruption. Google’s review-speed guidance explains both points.
The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Make each change easier to inspect
When practical, split an oversized change into smaller dependent changes so reviewers can understand and respond to each part sooner. Keep the dependency order and ownership clear; a chain of changes can create new waits if authors and reviewers cannot tell what must land first. If a change cannot be split, share it early for high-level design feedback so major concerns surface before detailed review. Google recommends splitting large changes where feasible. Its code-review guidance covers that practice.
Move repeatable feedback earlier; keep human judgment
Use continuous testing and CI to catch repeatable problems during development, and integrate appropriate security controls, monitoring, and observability into the delivery workflow. DORA recommends peer review during development supplemented by automation to detect, prevent, and correct bad changes early. Automation can check defined rules consistently; reviewers remain important for design, behavior, maintainability, and context-sensitive risk. Apply extra scrutiny where the risk warrants it rather than imposing the same review burden on every change. DORA’s change-approval guidance discusses peer review, automation, and risk-based validation.
When automation helps—and when to measure its effect
Automation is not automatically a shortcut to faster merging. A 2022 study by Wessel, Vargovich, Gerosa, and Treude examined 5,000 repositories: 1,489, almost 30% of the sample, had adopted GitHub Actions. The study reported more time to accept a PR after adoption in the repositories studied. This is an observed association, not proof that adoption caused delays across projects. It is a reason to measure the effect of a change in your own workflow, not to infer that CI is inherently harmful. The study abstract describes its findings.
Rank #4
Google Research’s 2024 paper describes a different use: an ML system that suggests edits to resolve review comments. In Google’s deployment, authors applied a suggested edit for 7.5% of all reviewer comments. The paper also reports about 60 minutes of average active author shepherding time per change between sending it for review and finally submitting it. That is Google-specific active author time, not total elapsed review time or an industry benchmark. The paper projects hundreds of thousands of engineer hours saved annually at Google’s scale; that projection is not a measured benefit for other organizations. These figures illustrate one deployment, not a general promise that review automation will reduce cycle time. Google Research’s paper describes the system and results.
When evaluating a workflow or platform, compare the features that address your measured delay: reviewer routing, queue visibility, support for small batches, CI feedback latency, risk-based checks, cross-time-zone handoffs, and the quality signals retained. Treat these as evaluation criteria, not as evidence that a particular tool will improve performance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Run a small experiment and protect quality
- Choose one bottleneck. Use the timestamp data to identify the largest avoidable wait, such as assignment delay or slow checks.
- Record a baseline. Track first-response time, time between rounds, check waiting, and total merge time. Include a quality signal such as change-failure rate, plus rework and reviewer interruption.
- Change one part of the flow. For example, clarify reviewer routing or move a reliable check earlier. Avoid changing several steps at once if you want to know what affected the result.
- Compare like with like. Check similar change sizes, risk levels, teams, and handoff patterns before and after the intervention.
- Keep or revise the change based on outcomes. Faster response is useful only if the change also preserves meaningful review, sustainable focus, and delivery quality.
DORA’s 2025 report describes research involving more than 100 hours of qualitative data and nearly 5,000 technology professionals. That breadth supports using its framework to ask better process questions; it does not establish a universal ideal PR review time for every organization. The 2025 report provides its research context. DORA also makes its research program available at dora.dev/research.
Account for sudden increases in code volume
When code production accelerates, review and integration can become the limiting steps. In an April 2026 first-person account, Google Cloud’s Lee Boonstra described a team where increased speed exposed a bottleneck in reviewing and integrating software, alongside cross-time-zone dependencies and merge conflicts. This is a useful example of how a workflow can become strained, not a comparative study or evidence that every team will see the same effect. Boonstra’s account describes that experience.
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.
Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problems
