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A pull request can be ready for feedback while its author waits for a first response, an approval, or a merge. Those are separate delays—and together they can make delivery feel slow even when coding is moving steadily. A review queue may be a constraint on your team, but the headline is a hypothesis to test, not a diagnosis to assume.
What “review time” hides
Measure the review path as distinct intervals. Otherwise, a single turnaround number can obscure where work is actually waiting.
- Time to first response: from the review request to the first human response. This shows how long a change waits before anyone engages.
- Time to acceptance: from the review request until the change is accepted. This includes the first-response interval and any discussion or revisions before approval.
- Time from acceptance to merge: from approval to integration. This can expose a separate queue or a manual step after the review decision.
These intervals are useful because they point to different possible causes. A slow first response may suggest reviewer availability or unclear ownership; a long acceptance-to-merge interval may call for examining handoffs or merge procedures. Treat those as leads to investigate, not explanations established by the timestamps alone.
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How to tell whether the queue is a delivery constraint
Start with your own baseline rather than an industry target. DORA’s 2023 guidance asks teams to examine time from code completion to review, average review batch size, the number of teams and geographic locations involved, and whether automation is improving quality based on review feedback. It warns that longer waits between code completion and review can reduce developer effectiveness and delivered software quality. DORA: Are code reviews your bottleneck?
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- Record the three intervals. Use consistent start and end events for first response, acceptance, and merge. Separate reopened or revised changes if your system makes that distinction.
- Compare the queue with the rest of delivery. Look at whether review waits change alongside overall lead time and quality. A long review interval matters differently if work is progressing elsewhere or if it is holding up dependent work.
- Inspect the shape of the work. Track review batch size, how many teams or locations are involved, reviewer availability and skills, and the number of handoffs.
- Check what can proceed safely while waiting. Identify whether the author or other contributors are blocked, and whether an accepted change still needs a person to perform a separate merge step.
- Review the role of automation. Ask whether automated checks are improving quality based on review feedback, and whether a manual step after acceptance is required by policy.
Read the numbers as a diagnostic, not a verdict. A queue is a plausible constraint when work repeatedly waits at review and that wait coincides with slower delivery or impaired developer effectiveness. Your team’s data must establish whether the relationship holds locally.
What the evidence says—and what it does not
Review is people-dependent engineering work
In a May 2015 Microsoft Research publication summary, Jacek Czerwonka and Michaela Greiler wrote: “Since they require involvement of people, code reviewing is often the longest part of the code integration activities.” The authors also emphasize reviewer skills and social context, and argue for more precise workflow guidance. This is a reason to treat review as substantive work that needs capable, available reviewers—not a current measurement of how long reviews take on every team. Microsoft Research: Code Review: A Study of Social Context and Guidance
Smaller batches are a practice to test, not a universal law
DORA recommends small batches as one approach that can improve review efficiency, feedback, and focus. But a 2023 University of Groningen doctoral thesis by Gunnar Kudrjavets reports negligible correlation between pull-request size or composition and time to merge in the context it studied. The findings are not necessarily contradictory: one describes a recommended practice, while the other reports an observed relationship in a specific setting. Neither establishes a rule for every team. Kudrjavets’s thesis also distinguishes the wait for a first response from the delay between acceptance and merge, and reports that respondents considered quick reaction important and time-to-merge a key review metric. University of Groningen: The Need for Speed: Increasing the Code Review Velocity
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Post-acceptance delays may matter, but the estimate is context-specific
A 2022 empirical study of waiting after acceptance in Phabricator projects estimated that addressing the measured post-acceptance delays could increase code velocity by 29–63% in those projects. That range is the study authors’ estimate for their data and conditions—not a general forecast for other teams or evidence that all review queues behave alike. The authors call for further work on the effects of review policy and defect density. University of Groningen: Mining Code Review Data to Understand Waiting Times Between Acceptance and Merging
AI changes the context, not the diagnosis
DORA’s 2025 report abstract describes more than 100 hours of qualitative data and survey responses from nearly 5,000 technology professionals worldwide, and characterizes AI as an amplifier of organizational strengths and dysfunctions. The abstract does not provide a code-review-queue statistic, so it cannot establish that AI has made review the bottleneck. For teams using AI-assisted development, measure whether production changes have altered the team’s actual review intervals and outcomes. DORA: 2025 State of AI-assisted Software Development Report
Experiments that can locate the cause
Change one part of the workflow at a time, then compare the same intervals and delivery outcomes against your baseline. Keep quality in view; reducing waiting is not a success if defects or risky approvals increase.
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Clarify reviewer ownership and capacity
Make it clear who is expected to respond, and assess whether the assigned reviewers have the right skills and time. If a change waits for one person, compare that pattern with changes routed to a broader or differently organized reviewer group. The goal is not to maximize the number of reviewers, but to test whether ownership or availability explains the wait.
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Split changes when doing so preserves a meaningful, reviewable unit of work. Compare response and acceptance intervals, feedback quality, and delivery outcomes; do not assume that smaller pull requests will automatically shorten time to merge. The Groningen thesis’s context-specific finding is a reason to measure rather than impose a universal size rule.
Reduce avoidable handoffs
Map what happens between request, decision, and merge. Where policy permits, simplify unnecessary routing or manual post-acceptance steps. Track acceptance-to-merge time separately so an improvement in reviewer response does not hide a lingering integration delay.
Use pairing or automation selectively
DORA identifies pair programming and loosely coupled teams, alongside small batches, as approaches teams can evaluate for review efficiency. Automation can also help when it improves quality based on review feedback. Apply these practices where they fit the work and governance, then assess both speed and quality rather than presuming a benefit.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to judge the result
After a small experiment, compare the same intervals and delivery measures you recorded before it. Look for which segment changed, whether any gain persists across the work you sampled, and whether quality held steady. If review waits shrink but overall delivery does not improve, the queue may not have been the binding constraint—or another delay may now dominate. If delivery improves but quality worsens, the change is not a clear win. The useful conclusion is the one your local evidence supports, not a promised productivity percentage.
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