Your Best Engineer Is About to Quit. (Check Their Review Load)

Code review is one of the most valuable activities in software development—but it's also one of the most unevenly distributed. In many teams, a handful of senior engineers handle the majority of reviews, creating bottlenecks, burnout, and knowledge silos.

This guide shows you how to identify review load imbalances, implement fair distribution strategies, and maintain review quality without burning out your best people.

Why Review Load Balance Matters

The Hidden Cost of Unbalanced Reviews

When review work concentrates in a few engineers, the consequences ripple through your entire engineering organization:

• Bottlenecks: PRs queue up waiting for the same 2-3 reviewers, slowing everyone's cycle time.
• Burnout: Overloaded reviewers spend 3-4 hours daily on reviews, leaving little time for their own work.
• Review quality decline: Exhausted reviewers rubber-stamp PRs they don't have time to properly review.
• Knowledge silos: When only a few people review code, the rest of the team doesn't learn the codebase.
• Attrition risk: Your most experienced engineers—the ones doing most reviews—are also the most likely to leave if burned out.

Quantifying the Impact

Consider a typical scenario:

This isn't just a fairness issue—it's a delivery issue. Unbalanced reviews directly slow your team's velocity.

Identifying Overloaded Reviewers

Signs of Reviewer Overload

Look for these warning signs that reviewers are overwhelmed:

• Long review queues: The same reviewers have 5+ PRs waiting
• Slow initial response: Time to first review exceeds 24 hours consistently
• Short review comments: "LGTM" or single-word approvals instead of substantive feedback
• Reviewer complaints: "I can't get anything done because I'm always reviewing"
• Delayed own PRs: Heavy reviewers' own work gets deprioritized

Manual Analysis: GitHub Insights

You can get basic review counts from GitHub's contributor insights:

1. Go to your repository → Insights → Contributors
2. Look at the "Reviews" tab to see who's reviewing most
3. Compare review counts across team members

Limitations: GitHub shows counts but not time spent, review depth, or trends.

Better Analysis: Review Network Visualization

A review network shows not just who reviews, but who reviews whom. This reveals patterns like:

• Concentrated reviewers: Single node with many incoming edges
• Review pairs: Two people who only review each other (clique)
• Isolated reviewers: Team members who rarely give or receive reviews

Strategies for Better Distribution

Strategy 1: Round-Robin Assignment

The simplest approach: assign reviewers in rotation. Each PR goes to the next person in the list.

Pros:

• Perfectly even distribution
• Easy to implement with CODEOWNERS or assignment bots
• No subjective decisions

Cons:

• Ignores expertise—a frontend PR might go to a backend specialist
• Doesn't account for availability (PTO, busy sprints)
• Can slow review quality if reviewers lack context

Best for: Small, generalist teams where everyone knows the whole codebase.

Strategy 2: Expertise-Weighted Assignment

Assign reviewers based on file ownership or past activity in the changed files, but distribute the load among multiple experts.

Example CODEOWNERS setup:
# Frontend - rotates among 3 experts
/src/components/ @frontend-team

# Backend - rotates among 3 experts
/api/ @backend-team

# Shared infrastructure - any senior engineer
/infra/ @senior-engineers

Pros:

• Reviews stay high quality (experts review their areas)
• Load distributes within expertise pools
• GitHub natively supports team-based CODEOWNERS

Cons:

• Requires maintaining accurate CODEOWNERS files
• Can still create bottlenecks if pools are too small

Strategy 3: Load-Aware Assignment

Assign to the team member with the fewest pending reviews who has relevant expertise. This dynamically balances load.

Implementation approaches:

• GitHub Actions: Build a workflow that checks pending review counts before assigning
• Review bots: Tools like PullApprove, Reviewbot, or LGTM can implement load-aware assignment
• Manual check: Before assigning, quickly check who has capacity

Strategy 4: Review Rotation Duty

Designate a "review duty" rotation where one person handles most reviews for a day or week, then rotates to someone else.

Pros:

• Clear accountability—you know who's "on duty"
• Allows deep focus when not on duty
• Ensures everyone takes turns

Cons:

• Heavy load for the person on duty
• Might lack expertise for some PRs

Best for: Teams that want to minimize context-switching for most people. For teams spread across timezones, see also our Async Code Review for Distributed Teams guide.

Implementing Review Rotation

Step 1: Assess Current State

Before changing anything, document your current distribution:

1. Pull 90 days of PR data
2. Count reviews per team member
3. Calculate the Gini coefficient or simply compare highest vs. lowest
4. Identify the top 2-3 reviewers handling most load

Step 2: Define Expertise Pools

Group your team by area of expertise:

Example pools for a full-stack team:

Frontend Pool:
  - Alice (senior) - any frontend PR
  - Bob (mid) - UI components, not API layer
  - Carol (junior) - simple styling/markup only

Backend Pool:
  - Dave (senior) - any backend PR
  - Eve (mid) - business logic, not infrastructure
  - Frank (junior) - simple CRUD endpoints

Full-Stack:
  - Alice, Dave - cross-cutting changes

Step 3: Set Up Assignment Rules

Configure your assignment based on chosen strategy:

For CODEOWNERS (GitHub native):

# .github/CODEOWNERS

# Default reviewers (team, not individual)
* @your-org/engineering

# Frontend (rotates within team)
/src/components/ @your-org/frontend-team
/src/pages/ @your-org/frontend-team

# Backend (rotates within team)
/api/ @your-org/backend-team
/services/ @your-org/backend-team

For review bots:

• Configure load balancing in the bot's settings
• Set maximum concurrent reviews per person (e.g., 3)
• Enable automatic reassignment if reviewer doesn't respond in 24h

Step 4: Communicate the Change

Review distribution changes can feel political. Communicate clearly:

• Share the data showing current imbalance
• Explain the impact on cycle time and burnout
• Emphasize this is about team health, not individual criticism
• Set expectations for response times across all reviewers

Step 5: Monitor and Adjust

After implementing, track these metrics weekly:

• Review count distribution (is it more even?)
• Time to first review (is it faster?)
• Review quality (are substantive comments still happening?)
• Cycle time (is overall delivery improving?)

Measuring Improvement

Key Metrics to Track

After implementing load balancing, track these metrics to measure success:

1. Review Distribution Evenness

Compare reviews per person before and after:

2. Time to First Review

More distributed reviews should mean faster initial response. Target: under 4 business hours for most PRs.

3. Overall Cycle Time

Better load balance should reduce queue time and improve total cycle time.

4. Review Depth

Monitor that quality doesn't suffer. Look for:

• Average comments per review (should stay stable or improve)
• Percentage of "LGTM-only" reviews (should decrease)
• Bug catch rate in reviews

What Good Looks Like

A well-balanced review culture shows:

• No one dominates: No single person handles more than 25% of reviews
• No one is excluded: Everyone on the team reviews at least 10% of PRs
• Fast response: 90% of PRs get first review within 4 hours
• Quality maintained: Average review has 2+ substantive comments
• No bottlenecks: No reviewer has more than 3 pending reviews

Continuous Improvement

Review load balancing isn't a one-time fix. Build it into your regular cadence:

• Weekly: Quick check on distribution metrics in team standup
• Monthly: Review trends and adjust pools/rules if needed
• Quarterly: Retrospective on review culture and process

The goal isn't perfect equality—it's sustainable, high-quality reviews that don't burn out your best people or slow down your team.

Related Guides

• Code Reviewer Best Practices — improve review quality alongside distribution
• Detecting Burnout Signals from Git Data — catch overload before it becomes burnout
• Reducing PR Cycle Time — balanced load is key to faster cycle times