Code reviews slow delivery when proposed changes arrive faster than reviewers can give them timely attention. The fix is to find where the queue is forming—not simply to demand faster approvals. Measure the wait for a first response separately from the full review-to-merge cycle, then address the specific source of delay while protecting review quality.
When is code review actually the bottleneck?
A pull request that waits is a symptom, not a diagnosis. It may be waiting for an initial acknowledgment, a qualified reviewer, another round of author changes, or the coordination needed to approve and merge it. Those delays call for different remedies.
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“The new bottleneck” is best treated as a possibility for a particular team, not a universal industry trend. The available evidence does not establish how many teams have code review as their top delivery constraint, or that AI-assisted code generation has broadly caused review backlogs. For example, a 2024 Google Research paper studies machine-learning suggestions for resolving review comments; it does not measure whether AI-generated code has increased review queues.
Measure the two clocks separately
Track at least two intervals for each change: time to first response, from the review request to the first meaningful human action, and time to merge, from request to completion. The first shows how long an author is initially blocked; the second captures the wider review and merge cycle. The practitioner study Does Code Review Speed Matter for Practitioners? discusses both quick reaction time and time to merge.
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Where your tools and data allow it, also record time to acceptance, review rounds, and the age of open requests. Compare distributions and aging items, not just averages: a small number of very old changes can disappear inside a healthy-looking mean. Separate business hours and time zones when possible, since elapsed calendar time and time reviewers are actually available are not the same.
- First response is slow, but later reviews move quickly: look at triage ownership, reviewer availability, and assignment.
- First response is prompt, but time to merge is long: inspect review-round delays, change size, unresolved disagreements, and merge handoffs.
- Some reviewers or authors consistently carry more of the queue: examine how work is assigned and distributed, not only the team-wide speed metric.
Why pull requests wait
Requests have no clear owner
If nobody knows who is responsible for the first look, a request can sit even when the team has enough expertise overall. Author-selected, rotation-based, and recommender-assisted assignment each have trade-offs: consider expertise, availability, workload, and fairness rather than optimizing for speed alone.
Reviewer attention is limited
Review competes with focused work. Interrupting a reviewer mid-task can impose a context-switching cost, while waiting until a natural break can protect concentration. Google Engineering Practices recommends responding at a reasonable break point and providing an update if a full review must wait. Its guide sets a maximum first-response time of one business day as Google’s own guidance, not a universal service-level target.
Large or unclear changes take longer to understand
A large change can demand more time to understand and make it harder to spot how its parts fit together. Google’s guidance recommends splitting very large changes when practical. Smaller pieces are not automatically faster, however: dependent changes can add coordination overhead, so split work where the pieces remain understandable and reviewable.
Each review round creates another wait
A request may receive an initial response promptly yet remain open through several cycles of comments, revisions, and follow-up. Delays can accumulate when either side is unsure who should act next. Google recommends timely responses across review rounds to keep authors unblocked; useful status updates can make the next step explicit when a complete review is not yet possible.
Improve response time without lowering the bar
- Choose an initial-triage owner or rotation. Make it clear who checks incoming requests and redirects them when the assigned reviewer is unavailable.
- Set a response expectation that fits your coverage. Define when the clock runs, who covers absences, and how exceptions are communicated. Google’s one-business-day maximum is a reference point from its engineering guidance, not a rule every team must adopt.
- Make an early response useful. Acknowledge the request, give a realistic review window, identify another reviewer if needed, or provide a high-level response that lets the author continue safely.
- Protect time for focused review. Encourage reviewers to handle requests at natural breaks instead of treating every notification as an immediate interruption.
- Keep changes understandable. Split oversized changes into coherent pieces when the coordination cost is justified, and make the purpose and context of each request clear.
- Review the whole path. Check whether faster first responses actually shorten time to merge, or simply move the wait to later review rounds.
Google’s guide warns that slow reviews can delay team work, frustrate participants, create pressure to accept weaker changes, and discourage cleanup or refactoring. These are the rationale given in Google’s guidance, not quantified effects that should be assumed for every organization. The guide also makes the priority clear: speed should not come at the expense of review standards.
Choose assignment and automation with care
Reviewer assignment should balance suitable expertise with availability and sustainable workload. A recommender may help locate knowledgeable reviewers, but it can also reinforce patterns in existing assignment data. Google Research’s study Systemic Gender Inequities in Who Reviews Code found that women in the Google setting studied completed 25% fewer reviews on average than men. The authors point to multiple systemic factors, including reviewer selection, recommendation systems, and differences in credentials; this Google-specific finding is a reason to measure your own process, not a claim about every organization.
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Automation is most defensible when it assists with a bounded task and leaves responsibility clear. In Resolving Code Review Comments with Machine Learning, Google Research reported that authors applied an ML-suggested edit to 7.5% of reviewer comments in the deployment described. The same report gives an average of about 60 minutes of active author shepherding time between sending changes for review and final submission in that Google workflow. These figures describe one setting, not a general benchmark or proof that automated review can replace accountable human review.
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If you test suggestions or other review assistance, evaluate adoption, correctness, and time saved in your own workflow. Keep it clear which decisions still require human judgment; a fast suggestion is not evidence that a change is safe.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the research can—and cannot—tell teams
A 2018 Google Research case study, Modern Code Review: A Case Study at Google, examined 9 million reviewed changes, alongside 12 interviews and a survey of 44 respondents. Those figures describe the Google study context, not a representative sample of all engineering teams. A separate 2023 practitioner survey, Does Code Review Speed Matter for Practitioners?, had 75 completed surveys: 39 industry participants and 36 open-source participants. It offers practitioner perspectives, not a measure of how prevalent review bottlenecks are across the industry.
Nor should rapid approval be used as a proxy for correctness. Microsoft Research’s Code Reviews Do Not Find Bugs. How the Current Code Review Best Practice Slows Us Down makes a deliberately provocative argument: code review often does not, by itself, find blocking functional issues. The paper emphasizes reviewer skills and social factors, rather than establishing that review never finds bugs. Its practical implication is to be precise about what a review is expected to do and how the process supports that work.
Fairness belongs in the measurement too. A 2022 Google Developers Blog post summarized Google-specific research estimating that excess review pushback was associated with more than 1,000 additional engineer hours per day. That is an estimate for Google’s setting, not an industry-wide calculation. Review speed should therefore be considered alongside workload, assignment patterns, and how review feedback is handled.
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