PR Review Cycles

Overview

The PR Review Cycles report in Span measures code review efficiency by tracking how many times pull requests go back and forth between authors and reviewers during the review process. This metric helps teams understand review complexity, identify bottlenecks, and improve their code review workflow.

What Is a Review Cycle?

Definition

A review cycle represents one complete exchange between a PR author and a reviewer. It counts how many times a pull request must be revised after receiving feedback before it can be merged.

In Practice

• 1 cycle: PR submitted → reviewer provides feedback → author fixes it → approved on first attempt

• 2 cycles: Multiple rounds of feedback and fixes before approval

• Higher numbers: More back-and-forth, indicating complex reviews or misaligned expectations

Why It Matters

Lower review cycles indicate:

• ✅ Clearer code quality — Fewer requested changes

• ✅ Better communication — Authors understand feedback on first pass

• ✅ Faster PR merges — Less time spent revising

• ✅ More efficient reviews — Clear expectations and standards

How It's Calculated

The Formula

The metric calculates the average number of review cycles across all merged pull requests.

For each PR:

Review Cycles = (First Approval Count) + (Commits After Feedback Count)

Where:

1. First Approval = 1 cycle

   • The first time a reviewer marks the PR as "APPROVED"

2. Each Feedback + Commit = 1 cycle

   • Any commit that occurs after feedback is received (COMMENTED or CHANGES_REQUESTED)

Excluded from calculation:

• Comments/approvals by the PR author themselves

• Merge commits

• Feedback from bot accounts

Trimmed Average

The average is trimmed at 8 cycles to remove statistical outliers:

• Any PR with more than 8 review cycles is capped at 8 for calculation

• Prevents extremely complex PRs from skewing team metrics

• Industry-standard approach for fair comparison

Example:

Raw PR cycles: [1, 2, 1, 3, 15, 2, 1]
Trimmed (capped at 8): [1, 2, 1, 3, 8, 2, 1]
Average: (1+2+1+3+8+2+1) / 7 = 2.57 cycles

What's Included

• ✅ Only merged pull requests

• ✅ Active contributors only (employees with recent commit activity)

• ✅ Based on your selected reporting period

• ❌ Excludes abandoned/closed PRs

• ❌ Excludes draft/WIP PRs

• ❌ Excludes inactive contributors

Accessing the Report

Navigation Path

Primary Route:

1. Login to Span

2. Under "Insights" in main navigation, click on "Productivity"

3. Select "Code Review" tab

4. Find "PR Review Cycles" metric

Alternative Routes:

• Team/Individual Pages: View each person's review cycle performance

• AI Transformation Report: Review cycles appear as a key AI impact metric

• Metric Explorer: Search for "PR review cycles"

Metrics & Visualizations

Primary Metric Display

Interpreting the Data

Benchmark Ranges

What the Numbers Tell You

Average = 0.8 cycles

• Strong code quality

• Reviewers rarely need to request changes

• Authors likely well-trained with clear PR descriptions

• Fast merge times expected

Average = 2.2 cycles

• Normal, healthy code review process

• Some back-and-forth is expected and beneficial

• Review process is balanced

Average = 4.5 cycles

• Excessive back-and-forth

• Possible causes: Unclear coding standards, poor PR descriptions, misaligned expectations

• Action: Review best practices training, clearer PR guidelines

P90 = 8+ (while average = 1.5)

• Most PRs are efficient, but ~10% are problematic

• Action: Investigate outlier PRs — may be complex refactoring or new engineers

• Consider: Should large PRs be broken into smaller chunks?

Important Context

⚠ This metric has "negative connotation"

• Higher numbers = worse performance

• Goals should aim to reduce review cycles, not increase them

Available Filters

Person & Team Dimensions

Technical Dimensions

Use Cases

1. Improving Onboarding

Scenario: New engineers have 4.2 average cycles; team average is 1.8

Action:

• Provide targeted training on codebase architecture patterns

• Share common review feedback points

• Educate on PR size best practices

• Create onboarding code review checklist

2. Comparing Teams

Scenario: Backend team has 2.1 cycles; Frontend team has 3.5 cycles

Questions to Ask:

• Does Frontend have more complex architecture?

• Do Frontend reviewers have clearer standards/linters?

• Are Frontend PRs larger (leading to more feedback)?

• Can Backend practices be shared with Frontend?

3. Measuring AI Impact

Scenario: Before AI adoption: 2.3 cycles → After AI adoption: 1.8 cycles

Interpretation:

• AI-assisted code required fewer review rounds

• Code quality improved (fewer issues found in review)

• AI code aligns better with team patterns

• Action: Consider expanding AI tool adoption

4. Identifying Review Friction

Scenario: Overall average is 1.8, but P90 is 7.2

Action: Investigate the top 10% of complex PRs

• Are they all refactoring work? (expected)

• Are they all from junior engineers? (training opportunity)

• Should these be broken into smaller PRs? (process improvement)

5. Code Review SLA Tracking

Goal: "We want average < 1.5 cycles by Q3"

Action:

• Track progress over weeks/months

• If trending up → investigate what changed

• If trending down → reinforce positive practices

• Celebrate when benchmark is hit

6. Sprint Retrospective Analysis

Scenario: Review cycles spike at end of sprint

Interpretation:

• Deadline pressure may lead to rushed initial submissions

• Action: Encourage earlier PR submissions or adjust sprint planning

Important Considerations & Limitations

Context Matters

1. PR Size Impact

• Larger PRs typically have more cycles (more surface area for feedback)

• Recommendation: View alongside "PR Diff Size" metric

• Action: Consider breaking large PRs into smaller ones

2. Code Complexity

• Complex refactoring naturally has more cycles

• Recommendation: Use "PR Type" and team context to interpret

• Note: Not all high cycles indicate problems

3. Reviewer Availability

• If reviewers are slow to respond, authors must wait longer between cycles

• Note: This metric measures back-and-forth count, not speed

• Recommendation: Combine with "Review Response Time" metric

4. Team Phase

• Onboarding periods naturally have higher cycles

• Post-training should show improvement

• New feature work may spike temporarily

What's Not Captured

❌ Time spent per review cycle — Use "Review Response Time" for this

❌ Reviewer effectiveness — Requires qualitative analysis

❌ Quality of feedback — Available in "Review Themes" feature

❌ Whether cycles were justified — Requires manual review

Data Timing

• Metrics update daily (2-4 hour delay from PR merge)

• Historical data available for full subscription period

• Very new PRs (< 1 hour) may not appear immediately

Known Outlier Scenarios

When Review Cycles Spike:

• End of sprint (deadline pressure)

• New feature work (more unknowns)

• New reviewer rotations (learning curve)

• Complex architectural changes

When Review Cycles Drop:

• Post-training improvements

• Team familiarity improves

• Better PR descriptions

• Smaller, more focused PRs

Best Practices for Reducing Review Cycles

1. Smaller Pull Requests (Highest Impact)

Target: 200-400 lines of code

• Reduces cycles by ~40%

• Easier to review thoroughly in one pass

• Faster feedback loops

2. Clear PR Descriptions

Include:

• What changed and why

• How to test

• Screenshots for UI changes

• Issue/ticket references

Impact: Reduces clarification cycles

3. Pre-Review Standards

Before submitting:

• Run linters/formatters

• Write tests (TDD reduces cycles)

• Self-review your code

• Check against team standards

Impact: Reduces "style" feedback cycles

4. Reviewer Training

Establish:

• Clear code review standards document

• "Required" vs "nice-to-have" feedback guidelines

• Scope boundaries (avoid scope creep in reviews)

Impact: Reduces unnecessary multiple-round feedback

5. Pair Programming for Complex Work

When to use:

• High-complexity features

• Architectural changes

• Refactoring work

Impact: Reduces cycles on complex work through upfront alignment

6. Architecture Documentation

Provide:

• Documented architectural patterns

• Design decision records (ADRs)

• Onboarding materials for new engineers

Impact: Reduces "wrong approach" feedback cycles

Related Metrics

View these metrics together for complete understanding:

Quick Reference Checklist

When analyzing PR Review Cycles, ask yourself:

•  What's our current average? Trending up or down?

•  How do we compare to similar teams (percentile)?

•  Are there specific individuals/teams with outlier numbers?

•  Are high cycles driven by PR complexity or review challenges?

•  What changed recently (new people, processes)?

•  Are we improving over time?

•  Should we implement smaller PRs or better descriptions?

•  How does this connect to overall cycle time and delivery goals?

Summary

The PR Review Cycles report provides visibility into the efficiency of your code review process. Use it to identify friction points, measure improvement initiatives, and maintain healthy engineering practices. Remember: the goal isn't to minimize all review cycles (some iteration is healthy), but to optimize for efficient, high-quality reviews that support fast, reliable delivery.

Healthy Target: 1.5 - 2.5 cycles average for most engineering teams.