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Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Code review is under pressure not simply because developers are producing more code, but because reviewers must still reconstruct a change’s purpose, dependencies and risks from a pull request. Salesforce Engineering says its own code volume rose by about 30% as AI-assisted coding expanded, while large pull requests grew beyond 20 files and 1,000 changed lines. That is a company-specific account, not an industry-wide measurement—but it illustrates why adding reviewers or automated comments alone may not fix a workflow built around smaller, more easily understood changes.
Why code review can become a bottleneck
A pull request (PR) is both a proposed change and a request for other people to understand and assess it. Reviewers need to determine what the author intended, how the change fits the codebase, which behavior it might affect, and whether tests and safeguards are adequate. A diff shows changed lines; it does not always make the conceptual shape of a change obvious.
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AI-assisted coding can alter how quickly code is created without automatically changing how teams review it. In its January 29, 2026 engineering article, Salesforce reported an approximately 30% increase in code volume and said pull requests regularly grew beyond 20 files and 1,000 changed lines. The company also reported quarter-over-quarter increases in review latency and plateauing or declining review time for its largest PRs. These are Salesforce’s internal observations; they do not establish the same trend, scale or cause at other organizations. Salesforce Engineering’s account frames the risk as reduced scrutiny when change volume grows.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteLarge changes can also cross boundaries: backend logic, configuration, tests and user-facing components may all change together. When the review interface presents these as a long sequence of files, reviewers have to infer how the pieces relate. The workload is therefore not just counting lines. It is understanding scope and context while multiple changes compete for limited reviewer attention.
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What the available measurements do—and don’t—show
Different studies measure different parts of the review process. They should not be collapsed into a single claim that automation makes review faster or that every team has a capacity problem.
| Evidence | What was reported | How to interpret it |
|---|---|---|
| Salesforce Engineering, 2026 | About 30% growth in code volume; PRs regularly exceeding 20 files and 1,000 changed lines; rising quarter-over-quarter latency and plateauing or declining review time on the largest PRs. | Company-reported internal observations, not an industry estimate or proof that AI alone caused the changes. Source. |
| Google Research, 2024 | Millions of reviewer comments annually; authors averaged about 60 minutes of active shepherding between submitting a change for review and submitting it finally. In a deployment, 7.5% of reviewer comments were addressed using an ML-suggested edit. | The 60 minutes measures authors’ active work, not elapsed time waiting for review. Suggested-edit use is not a measure of review speed or correctness. Source. |
| Empirical Software Engineering study, 2024 | A survey of 75 participants—39 from industry and 36 open-source contributors—reported a median maximum acceptable review size of 800 source lines of code. | This is a survey finding, not a universally safe PR limit. The paper also discusses response time and scheduling review time. Source. |
| Cihan et al., arXiv preprint, 2024 | In a case study of 4,335 PRs across three projects, 1,568 had automated review; 73.8% of automated comments were resolved. Average PR closure duration was 5 hours 52 minutes before versus 8 hours 20 minutes after automated review. | Results varied across projects. The comparison does not prove the tool caused longer closure times, and comment resolution is not evidence of accuracy or faster delivery. Source. |
The evidence points to a useful distinction: activity inside a review, elapsed time to a decision, and the quality of that decision are separate outcomes. A comment can be resolved without being valuable; a review can be thorough without being quick; and a faster merge does not by itself establish fewer defects.
Redesign the workflow around understandable changes
There is no evidence here for one universal PR-size limit or a single best review system. Teams can instead address the sources of review effort directly: change coherence, context, reviewer availability and whether automated signals are useful enough to trust.
Keep changes conceptually coherent
Split work when a change combines independently understandable tasks, but do not split merely to hit a line-count target. Artificially dividing a tightly coupled change can make reviewers reconstruct the whole feature across several PRs. A good boundary lets a reviewer answer a focused question while retaining enough context to judge the change’s effect.
The practitioner survey’s 800-source-line median describes what respondents considered a maximum acceptable size; it is not a prescription. File count and line count are rough signals. A small diff in a risky authentication path may need more scrutiny than a larger mechanical update.
Give reviewers the context the diff omits
Authors can state the intended behavior, relevant design decisions, affected components, test evidence and known trade-offs in the PR description. Review systems can make related changes and ownership easier to find. Salesforce describes its internal system, Prizm, as grouping changes semantically and bringing in codebase and historical context alongside risk signals. The company says its approach uses asynchronous analysis while leaving decisions to people; this is a description of its own implementation, not independent validation of its effectiveness. Salesforce’s description of Prizm illustrates one design direction rather than a generally available product recommendation.
Make review capacity visible
When PRs wait, adding more code or reviewers does not necessarily resolve the queue. Track where time is spent: time to first response, time to acceptance or merge, and author effort spent responding to comments. The practitioner study identifies review response time, scheduling and development process as concerns. Google’s 60-minute figure is active author shepherding, not a benchmark for total turnaround. Together, these measures can distinguish a lack of reviewer availability from repeated rework, unclear ownership or changes that arrive too large to assess promptly.
Use automated review as assistance, not approval
Automation can surface possible issues early, suggest edits or help organize a large change. But a higher volume of signals can also create noise. In the industrial case study, 73.8% of automated comments were resolved; that rate does not say whether the comments were correct, whether the fixes prevented defects, or whether the remaining comments were false positives. The authors also identify faulty or irrelevant comments as a drawback to consider. The case study is evidence about one setting, not a guarantee for another.
Best Value
Salesforce’s authors, Shan Appajodu and Ravi Boyapati, describe the risk this way: “At scale, the primary risk of AI-generated code is not uniformly poor quality, but diminished scrutiny.” They say their response was to rebuild review around how developers reason about changes rather than automate judgment. Those statements explain Salesforce’s rationale; they are not universal empirical findings. Read the company account.
- Evaluate comment usefulness: sample automated findings and record whether reviewers accept, reject or ignore them, including recurring false positives.
- Keep accountability explicit: automation can advise, but a named human reviewer should remain responsible for approval and risk-sensitive judgment.
- Check the workflow effect: compare time to first response, time to merge and rework—not just the number of comments resolved.
- Choose where analysis runs: asynchronous checks can avoid blocking a reviewer’s first look, while urgent or high-risk checks may warrant an explicit gate. Decide based on the consequences of delay and missed issues.
How to tell whether a change is working
Compare the old and new workflow on the same kinds of changes where possible. A useful assessment includes PR size and conceptual coherence, access to architectural and historical context, time to first response and merge, whether automated analysis blocks the process, comment usefulness and false-positive burden, and clarity about who approves the change.
Do not treat a single metric as the objective. Faster closure can come at the expense of scrutiny; more resolved comments can mean more useful feedback or simply more edits. A better workflow helps people understand the change, directs attention toward meaningful risk, and makes waiting or rework visible without handing final judgment to an automated system.
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