Code review metrics seem like an objective way to measure team performance. Engineering managers look at turnaround time, number of comments, lines of code changed, or approval speed and try to draw conclusions about productivity, quality, or collaboration. But the same metrics that appear neutral can easily distort behavior, create perverse incentives, and reduce the very outcomes you want. Using them carefully means understanding what each metric actually measures, what it misses, and how to avoid common traps.

The Turnaround Time Trap

Turnaround time is the time between when a review is requested and when it receives a final decision. It is one of the most common metrics because it seems to measure responsiveness. A low average turnaround time looks good. But chasing lower turnaround time can pressure reviewers to approve changes faster than they should, or to leave superficial feedback. A reviewer who rushes through a complex change might miss logic errors or security issues. The metric also ignores the size and risk of the change. A one line typo fix should be reviewed quickly, while a database migration deserves more time. When managers push for faster turnaround across the board, reviewers may sacrifice thoroughness. Instead of focusing on the average, look at outliers. A change that sits unreviewed for three days might indicate a bottleneck. A change that gets approved in ten minutes on a critical path might indicate insufficient attention. Use turnaround time as a signal to start a conversation, not as a target to optimize.

Comments Per Review Does Not Measure Thoroughness

Counting the number of comments left on a review is another common habit. More comments is supposed to mean more thorough feedback. But this metric is easy to game and often meaningless. A reviewer can leave fifty trivial nitpicks about formatting that add no value, while a different reviewer might leave three detailed comments that catch real design issues. Comment count also ignores the quality of the feedback. A comment that says “Please add a null check here” is far more useful than “Lint this file”. The metric also penalizes reviewers who provide inline suggestions or approve clean code quickly. A better approach is to look at the nature of comments. Are they about logic, architecture, or security? Or are they about style preferences that should be handled by automated formatting? Engineering managers can spot patterns by reading reviews rather than counting comments. If you must use a number, consider tracking the ratio of substantive comments to total comments, but even that requires manual categorization. The safest move is to avoid comment count as a metric altogether.

Churn Rate and Rewrites

Churn rate measures how many lines of code are changed during a review cycle after the initial submission. High churn might indicate that the author submitted code too early, or that reviewers have conflicting opinions. Low churn might mean reviewers are not pushing back enough. But churn is not inherently good or bad. Some changes legitimately need multiple rounds because the original design was wrong. Others need zero changes because the code was already correct. Using churn as a quality signal can discourage authors from requesting early feedback, because they fear the metric will look bad. It can also make reviewers reluctant to ask for meaningful changes. A more useful practice is to analyze the reasons behind churn. Was the churn caused by a single large redesign request? Or by many small incremental fixes? The former might indicate a communication gap, while the latter is healthy iterative improvement. Track churn qualitatively, not quantitatively. Discuss it in retrospectives.

Approval Rate and Authority

Approval rate tells you what fraction of reviews are approved rather than rejected or closed without merge. A high approval rate might signal that reviews are rubber stamps. A low approval rate might signal excessive gatekeeping or unclear standards. But approval rate alone tells you nothing about the appropriateness of the decisions. Some teams have a culture where almost everything eventually gets approved after discussion, so the rate approaches one hundred percent. Other teams, especially those with strict security or safety requirements, may reject a significant portion of changes. The metric is meaningless without understanding the decision criteria and the risk context. A better signal is the number of reviews that require more than one round of significant changes, especially when the changes are unexpected. That indicates misalignment between author and reviewer expectations. Managers should focus on whether the review process catches issues before they reach production, not on whether the approval rate is high or low.

Lines of Code Reviewed per Hour

Some managers try to measure reviewer productivity by counting how many lines of code a reviewer processes per hour. This is one of the most dangerous metrics. A reviewer can skim a thousand lines of trivial boilerplate in an hour, but a hundred lines of complex threading logic might take three hours. The metric encourages reviewers to choose easy reviews and avoid hard ones. It also ignores the cognitive effort of understanding context, verifying tests, and checking for edge cases. Do not use this metric. If you need to understand reviewer workload, track the number of open reviews and the typical review queue length. That gives you a sense of capacity without creating perverse incentives. You can also ask reviewers directly whether they feel overloaded or underutilized.

Time to First Response

Time to first response is the delay before a reviewer leaves the first comment or approval. It is sometimes used as a proxy for responsiveness. But similar to turnaround time, it pressures reviewers to reply quickly even if they have not fully analyzed the code. A reviewer can leave a quick “looks good, will review more carefully” and then take another hour, which looks good on the metric but wastes everyone’s time. The metric also fails to account for time zones, meeting schedules, or focused work blocks. Instead, foster a norm where reviewers acknowledge a request within a reasonable window and provide a realistic estimate of when they will do the full review. That improves predictability without rushing the review.

The Danger of Comparing Teams

Engineering managers often compare code review metrics across teams to identify best practices or performance gaps. This is rarely valid. Different teams work on different codebases with different risk profiles, complexity, and norms. A team that maintains a legacy payment system will have slower review times and more comments than a team building a new internal tool. Comparing their metrics directly leads to false conclusions. The same applies to comparing the same team across quarters without accounting for changes in project scope, personnel, or tech stack. Any metric comparison must be adjusted for context. The safest approach is to use metrics only to track a team’s own trends over time, and only after defining what “good” looks like for that specific team’s domain.

What to Measure Instead

Given the risks of common code review metrics, what can you safely track? Focus on signals that are hard to game and directly tied to outcomes. One useful metric is the number of defects found during review that would have otherwise escaped to testing or production. You can track this by asking reviewers to tag comments with a severity label. Another signal is the percentage of reviews that include changes to tests or documentation. That indicates whether the team is verifying and communicating impact. You can also measure review distribution: are reviews evenly spread across the team, or do a few people carry the load? That helps with coaching and capacity planning. Finally, consider tracking the time between when a review is opened and when it receives its first meaningful review. A meaningful review is one that goes beyond style comments. That gives you a sense of how quickly the team engages with complex work without punishing careful reviewers.

Using Metrics to Start Conversations, Not Make Decisions

The most important principle is to never use code review metrics in isolation for performance evaluations, promotions, or punishment. Metrics should be conversation starters. If you see a review that sat for two days without feedback, ask the team what caused the delay. It might be a legitimate reason like a holiday or a blocked reviewer, or it might be a process issue. If you see a reviewer who leaves very few comments, ask them whether they find the reviews thorough enough. They might be reviewing offline or providing feedback verbally. Always pair quantitative data with qualitative understanding. That is the only way to avoid the metric traps.

Building a Healthy Review Culture Without Metrics Pressure

The ultimate goal of code review is knowledge transfer, defect prevention, and shared ownership. Metrics should serve that goal, not override it. When managers apply pressure through numbers, they undermine the trust and collaboration that make reviews valuable. Instead, foster a culture where reviewers and authors feel safe to ask questions, admit gaps, and invest time in thorough reviews. Celebrate reviews that catch subtle bugs, not reviews that finish in five minutes. Recognize reviewers who take time to write clear explanations, not those who approve the most changes per day. If you must measure something, measure whether the team feels the process is helping them ship better software. That is the only metric that matters.