The Exact Number of Reviewers Per PR (Research Says 2, But...)

One reviewer misses bugs. Five reviewers creates diffusion of responsibility where everyone assumes someone else will catch issues. The answer is two. Two reviewers is the right number for most pull requests.

This guide presents the research on optimal reviewer counts, shows you how to identify under-reviewed PRs in your codebase, and provides strategies for matching review depth to risk level.

The Goldilocks Problem: Too Few vs Too Many

The Risks of Too Few Reviewers

Single-reviewer PRs create several problems:

• Single point of failure: If the reviewer has a blind spot, bugs slip through
• Knowledge silos: Only two people understand the change (author + reviewer)
• Review quality variance: Some reviewers are more thorough than others
• No cross-pollination: Junior developers miss learning opportunities

This is especially risky for large or complex PRs where more eyes would catch more issues.

The Risks of Too Many Reviewers

But adding more reviewers isn't always better:

• Diffusion of responsibility: "Someone else will catch it" mentality
• Slower cycle time: Waiting for multiple approvals extends PR lifetime
• Review fatigue: When everyone reviews everything, quality drops
• Conflicting feedback: Multiple reviewers may give contradictory suggestions
• Notification overload: Team members get flooded with review requests

What Research Says

Research on code review effectiveness shows sharp diminishing returns after 2 reviewers:

"Three reviewers is political theater dressed up as quality assurance."

Industry Benchmarks for Reviewer Count

The Two-Reviewer Standard

Two reviewers is the standard. Here's how to adjust based on PR type:

"The third reviewer typically rubber-stamps because they assume the first two were thorough."

Team Size Considerations

Your team size affects what's sustainable:

• Small teams (3-5 devs): 1 reviewer works here. You can't afford everyone reviewing everything.
• Medium teams (6-12 devs): 2 reviewers is sustainable and catches most issues.
• Large teams (13+ devs): 2 reviewers with CODEOWNERS to ensure domain expertise. Don't add a third.

How CodePulse Flags Under-Reviewed PRs

Large PRs with insufficient review coverage are high-risk. CodePulse automatically detects this pattern.

What to Look For

When reviewing your Risky Changes page, pay attention to:

• Repeat offenders: Are certain repositories or developers consistently getting single-reviewer approval on large PRs?
• Critical code paths: Are changes to authentication, payments, or infrastructure getting enough review?
• Timing patterns: Are under-reviewed PRs happening at certain times (end of sprint, before deadlines)?

Using Review Network Data to Balance Coverage

Reviewer count alone doesn't tell the full story. You also need to consider who's doing the reviewing.

Review Network Visualization

CodePulse's Review Network shows the connections between authors and reviewers:

• Node size: Indicates how much review activity a developer has (larger = more reviews)
• Edge thickness: Shows how frequently two developers review each other's code
• Clustering: Reveals sub-teams or knowledge silos

Healthy vs Unhealthy Patterns

"If you require three approvals 'for safety,' you're optimizing for the appearance of rigor, not actual quality."

Using Network Data to Improve Coverage

1. Identify bottlenecks: If one person reviews most PRs in an area, train backups
2. Break silos: Encourage cross-team review to spread knowledge
3. Develop reviewers: Pair junior developers with seniors on reviews

Configuring Alerts for Review Gaps

Don't wait for post-mortems to discover review coverage gaps. Set up alerts to catch patterns early.

Recommended Alert Configurations

Alert: Under-Reviewed Large PRs
  Metric: merge_without_approval_rate_percent
  Operator: >
  Threshold: 10
  Severity: warning
  Description: "More than 10% of PRs merging without approval"

Alert: Low Review Coverage
  Metric: review_coverage_percent
  Operator: <
  Threshold: 90
  Severity: warning
  Description: "Less than 90% of PRs receiving review"

Alert: Single Reviewer Dominance
  Description: "Track if one person is handling too many reviews"
  Note: Monitor via the Review Network page and
        developer-level review_load_ratio metrics

Alert Best Practices

• Start with loose thresholds: Understand your baseline before tightening
• Alert on trends, not incidents: One under-reviewed PR is a miss; a pattern is a problem
• Route to the right people: Send review alerts to tech leads, not individual contributors

For more on alerts, see our Alert Rules Guide.

Reviewer Assignment Strategies

Strategy 1: CODEOWNERS

GitHub's CODEOWNERS file automatically requests reviews from designated owners:

# .github/CODEOWNERS

# Default owners for everything
* @team-leads

# Frontend code
/src/components/   @frontend-team
/src/pages/        @frontend-team

# Backend API
/api/              @backend-team

# Critical paths - require senior review
/api/auth/         @security-team
/api/payments/     @payments-lead @security-team

Strategy 2: Round-Robin Assignment

Distribute review load evenly by rotating through team members:

• Pros: Fair distribution, everyone develops review skills
• Cons: May not match expertise with PR content
• Best for: Teams where everyone can review most code

Strategy 3: Expertise-Based Assignment

Match reviewers to PRs based on knowledge of the changed files:

• Pros: Higher quality reviews, catches domain-specific issues
• Cons: Creates bottlenecks around experts
• Best for: Large codebases with specialized areas

Strategy 4: Hybrid Approach

Combine strategies based on PR characteristics:

Why Not the "Plus One" Rule?

Some teams use a "plus one" rule: request one more reviewer than you think you need. We disagree.

This rule sounds safe but creates problems:

• It normalizes over-reviewing, which slows everything down
• The "extra" reviewer often rubber-stamps
• It masks the real question: do you trust your reviewers?

Instead of adding reviewers, improve the reviewers you have. Train them. Give them context. Trust them. Two thorough reviewers beat three distracted ones.

Measuring Review Coverage Improvement

After implementing the two-reviewer standard, track these metrics to verify it's working:

• Average reviewers per PR: Target 1.8-2.2 for standard PRs
• Review coverage %: Should be at or above 95%
• Large PR single-reviewer rate: Should approach zero
• Review load distribution: No one person should handle more than 30% of reviews

The goal isn't more reviewers. It's the right number of reviewers, consistently applied. Two is enough. Focus on making those two reviews count.

For more on improving your review process, see our guides on Review Load Balancing, PR Cycle Time Benchmarks, and Reducing PR Cycle Time.