A pull request can be ready for review while its author waits for a response, then wait again after approval before it merges. Those delays may constrain delivery even when engineers are writing code quickly—but the title is a hypothesis to test, not a diagnosis that fits every team. Measure where work waits and compare those intervals with the rest of your delivery flow.
What “review time” actually includes
One headline metric can hide different problems. Separate the elapsed time into three intervals:
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- First-response time: from proposing changes until a reviewer first responds.
- Time to acceptance: from the proposal until the changes are accepted. This includes any additional review rounds after the first response.
- Post-acceptance time: from acceptance until merge.
These distinctions matter because the remedies differ. A long first-response interval may point to reviewer availability or unclear ownership; repeated review rounds may involve the change, reviewer skills, or feedback quality; a long post-acceptance interval may point to a handoff or a manual merge step. These are possibilities to investigate, not causes you can infer from elapsed time alone.
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Start with your team’s own pull-request timestamps and examine the distribution, not just a single average. A few unusually long requests can distort the average, so look at the median and the spread as well. Segment by repository, change type, team, or location if those differences could affect comparisons, and use the same definitions and time window when you establish a baseline and assess a change.
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- Define the timestamps. Record when work is proposed, when the first human response arrives, when it is accepted, and when it merges. State how you handle reopened requests, draft changes, weekends, and requests that are abandoned.
- Calculate each interval separately. Compare first-response time, time to acceptance, and time from acceptance to merge. Do not label their sum simply “review time” without showing what it contains.
- Check the surrounding workflow. Compare review waits with the time spent coding, testing, deploying, and otherwise waiting. Ask whether an author can safely move on while a request is waiting and whether accepted work still depends on a person to merge it.
- Look for structural patterns. Examine review batch size, reviewer availability and skills, team and geographic handoffs, and what quality checks are automated. DORA recommends investigating these dimensions when asking whether reviews are a delivery bottleneck. DORA’s code review guidance
- Track outcomes alongside speed. Check whether changes to the workflow coincide with shifts in delivery lead time and quality. Faster merges alone do not establish that the process improved.
What the evidence says—and what it does not
DORA’s 2023 guidance treats review delay as a capability teams can investigate. It calls attention to the time between code completion and review, review batch size, the number of teams and locations involved, and whether automation improves quality using feedback from reviews. DORA says a longer wait between completion and review can reduce developer effectiveness and delivered software quality. It points to small batches, loosely coupled teams, and pair programming as approaches teams can evaluate to improve review efficiency—not as guaranteed fixes. DORA’s code review guidance
A 2023 University of Groningen doctoral thesis distinguishes time from proposing a change to its first response from time between acceptance and merge. In the context studied, it reports negligible correlation between pull-request size or composition and time to merge. That finding does not show that batch size never matters: it concerns a particular context and a particular outcome, while DORA recommends small batches as a way to support feedback, efficiency, and focus. Treat batch size as a variable to test locally, not a universal explanation for queue length. Kudrjavets, The Need for Speed: Increasing the Code Review Velocity
A 2022 empirical analysis of Phabricator projects estimated that addressing the post-acceptance delays measured in those projects could increase code velocity by 29–63%. That is a study-specific estimate, not a productivity forecast for other teams. The authors also call for further work on review policy and defect density, underscoring why speed should not be considered in isolation. Mining Code Review Data to Understand Waiting Times Between Acceptance and Merging
Code review is also skilled human work, not just a queueing step. A 2015 Microsoft Research publication summary by Jacek Czerwonka and Michaela Greiler notes that review can be a long part of integration because it requires people, and argues that reviewer skills and social context matter. It is not a current measurement of review time across teams. Microsoft Research publication summary: Code Review at Large
AI-assisted coding adds a reason to keep measuring rather than assume. DORA’s 2025 report abstract describes AI as an amplifier of organizational strengths and dysfunctions and reports survey responses from nearly 5,000 technology professionals worldwide. The abstract does not provide a code-review-queue statistic, so it cannot establish that AI has made review the bottleneck. DORA 2025 State of AI-assisted Software Development Report
How to test whether review is your constraint
Use your baseline to choose one small workflow change. Keep the observation period and timestamp definitions consistent, and compare both delivery outcomes and quality before and after. Possible experiments include:
- Make reviewer ownership clearer. Assign responsibility so a request does not wait because it is unclear who should respond. Check whether first-response time changes without simply overloading one reviewer.
- Reduce avoidable handoffs. Examine whether a request crosses teams or locations unnecessarily, and whether a more loosely coupled workflow can reduce waiting while preserving the needed expertise.
- Test smaller changes. Try smaller review batches if they suit the work, then assess feedback cycles, review effort, lead time, and quality. The evidence does not support assuming smaller batches will reduce merge time in every context.
- Automate only safe steps. If an accepted change waits solely for a manual merge step, consider automating that step only where your policies and checks permit it. Automation does not resolve a missing decision or reviewer capacity.
- Use pairing where appropriate. Pair programming can move feedback earlier for suitable work, but assess its fit and effect rather than treating it as a universal replacement for review.
Change one factor at a time when practical. If several changes land together, you may improve the workflow but will have a harder time identifying which change mattered. Also watch for displacement: a shorter first-response interval is not an improvement if acceptance takes longer, post-acceptance waits grow, or quality worsens.
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Read the result as a team-design question
A queue can reflect individual attention, but it can also expose how work is assigned, divided, handed off, and integrated. If one specialist is the only person able to review certain changes, the constraint may be reviewer capacity or knowledge distribution. If accepted requests wait to merge, adding reviewers may not address the delay. Use the interval where time accumulates to decide what to investigate next, then validate the explanation with another measurement.
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