The PR Comment Sweet Spot (Hint: It's Not Zero)

How many comments should a code review have? Too few might mean rubber-stamping. Too many might signal unclear code or overly pedantic reviewers. This guide provides benchmarks for healthy review comment volumes and how to interpret what your team's patterns reveal.

Why Review Comment Volume Matters

Review comments are one of the few signals we have into review quality. While they don't tell the whole story, comment patterns can reveal:

• Review depth: Are reviewers actually reading the code, or just clicking approve?
• Code clarity: Does the code require extensive explanation, or is it self-documenting?
• Team knowledge sharing: Are reviews being used as learning opportunities?
• Process health: Is the team engaged in meaningful code review?

The Goldilocks Problem

Review comments follow a "Goldilocks" distribution—you want them just right:

Industry Benchmarks by Team Type

Comments Per PR

Based on industry research and analysis of high-performing teams:

Comments Per Reviewer

Looking at individual reviewer patterns:

• 0 comments consistently: Reviewer may not be engaged—investigate
• 1-2 comments average: Light touch, possibly appropriate for experienced teams
• 3-5 comments average: Healthy engagement level
• 10+ comments average: May be blocking velocity or being overly pedantic

Important Caveats

These benchmarks are starting points, not targets. Context matters enormously:

• PR size: A 500-line PR should have more comments than a 20-line fix
• Author experience: Junior developers benefit from more feedback
• Code area: Critical paths deserve more scrutiny
• Team culture: Some teams prefer verbal discussion over written comments

When Comments Are Too Few

Signs of Rubber-Stamping

• PRs approved within minutes of opening
• Consistently 0 comments across all PRs
• High approval rate with no requests for changes
• Reviewers approve PRs outside their expertise

Why It Happens

• Time pressure: "Just ship it" culture deprioritizes review
• Social dynamics: Reluctance to critique senior developers
• Reviewer overload: Too many reviews, not enough time
• Unclear expectations: Team hasn't defined what good review looks like

How to Address

1. Set explicit review expectations (e.g., "Check for X, Y, Z")
2. Track review time alongside approval time—instant approvals warrant investigation
3. Rotate reviewers to bring fresh perspectives
4. Celebrate thoughtful reviews publicly

When Comments Are Too Many

Signs of Over-Commenting

• PRs stuck in review for days with ongoing back-and-forth
• Developers feeling demoralized by extensive feedback
• Nitpicky comments on style rather than substance
• Same issues raised repeatedly across PRs

Why It Happens

• PRs too large: Big changes invite more comments (see our PR size guide)
• Unclear standards: No linting or formatting automation
• Knowledge gaps: Author unfamiliar with codebase patterns
• Perfectionism: Reviewers holding to impossible standards

How to Address

1. Automate style checks: Let linters handle formatting so humans focus on logic
2. Break up large PRs: Smaller changes mean fewer comments
3. Distinguish blocking vs non-blocking: Use conventions like "nit:" for optional suggestions
4. Document patterns: Create team guidelines to reduce repeated feedback

Comment Quality Over Quantity

The number of comments matters less than their quality. A single insightful comment about architecture is worth more than ten nitpicks about variable names.

High-Quality Comments

• Explain the "why" behind suggestions
• Offer alternatives, not just criticisms
• Ask clarifying questions
• Acknowledge good patterns ("Nice use of X here")
• Focus on maintainability and correctness

Low-Quality Comments

• Style nitpicks that linters could catch
• Vague criticism without actionable feedback
• Comments that could be resolved with documentation links
• Demands without explanation
• Consistently negative tone

For more on building a healthy review culture, see our guide on Code Review Culture and Sentiment.

Tracking Review Metrics

Key Metrics to Monitor

Segmenting the Data

Raw averages can be misleading. Segment your data by:

• PR size: Compare comment counts within size buckets
• Author seniority: Juniors should receive more feedback
• Code area: Critical paths vs routine changes
• Reviewer: Identify outliers (too many or too few comments)

Building a Constructive Review Culture

Setting Expectations

1. Define "good enough": Not every PR needs to be perfect. Define what blocking issues look like.
2. Time-box reviews: Set SLAs for review turnaround to prevent endless back-and-forth
3. Balance load: Use review load balancing to prevent burnout

Encouraging Thoughtful Feedback

• Recognize developers who give helpful reviews (not just volume)
• Share examples of great review comments in team meetings
• Create a review guide with common patterns and how to address them
• Pair junior developers with senior reviewers for mentorship

Using Data Without Micromanaging

Review metrics should inform team discussions, not individual performance reviews. Use them to:

• Identify systemic issues (e.g., "Our PRs are too big")
• Spot opportunities for automation (e.g., "These comments are always about formatting")
• Guide process improvements (e.g., "Reviews take too long in this area")

Avoid using comment counts to judge individual reviewers—this encourages gaming the metric rather than improving quality.