Cutting PR review time is an orchestration problem, not a reviewer problem
The usual answer to "how do we reduce PR review time" is to make the reviewer faster. Vendor pages recommend an AI reviewer that reads a diff in seconds and posts comments, so the bottleneck moves. A new field study from a real industrial repository says the lever is somewhere else entirely: how you slice the change into commits, batches, and CI jobs. The preprint arXiv:2609.29172 ,…
The article titled "Cutting PR review time is an orchestration problem, not a reviewer problem" explores the reasons behind long code review times in large software repositories. The primary focus is on the fact that AI-assisted code remediation tools, such as an experienced developer using a command-line AI coding buddy, do not significantly reduce review times.
Instead, the bottleneck is found in the way changes are sliced into commits, batches, and CI jobs. A 15-day field study conducted in a closed-source industrial C++ codebase reveals that when code editing becomes cheap, the number of commits generated increases, leading to a high volume of commits touching thousands of lines. This, in turn, causes CI and reviewer saturation, making the review process less efficient.
The study suggests that CI capacity, review effort, and change orchestration become the primary bottlenecks once mechanical editing becomes cheap. The paper emphasizes that review time is not the metric vendors advertise, as it primarily reflects the time taken for automated comments rather than the overall review process. The authors propose that the major issue lies in the orchestration around the review process, including commit batching decisions, CI job counts, build and static-analysis failures, negotiation about granularity, and re-review loops after follow-up fixes.
To address this, the study recommends checking the pipeline for where latency actually occurs before purchasing additional review tools. If the surge of AI-generated commits is impacting CI and reviewer attention, the first step should be to control commit and batch granularity, such as capping the number of files per change, batching by directory or semantic intent, and ensuring CI has bounded jobs.
Additionally, negotiating what a reviewable unit looks like with the team before the volume arrives is crucial.
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