Code churn—code that gets written, then modified or deleted shortly after—is often treated as a simple productivity metric. "Developer X has high churn; they must be writing buggy code."
This interpretation is almost always wrong.
Churn is a symptom, not a diagnosis. And the underlying causes usually have nothing to do with individual developer skill.
What Churn Actually Measures
Churn captures code that changes within a window after being written. Velocinator uses a 21-day window by default:
Rework Rate = Lines modified or deleted within 21 days / Total lines written
A 20% rework rate means one in five lines changed shortly after being written. Is that good or bad? It depends entirely on why.
The Causes of Churn
Cause 1: Unclear Requirements
The most common cause. Developer builds what they understood. Product reviews it and says "that's not quite right." Developer rebuilds.
This isn't a code quality problem—it's a communication problem. The churn is just making the communication failure visible.
Symptoms:
- Churn spikes after product review or demo
- Concentrated in feature work (not infrastructure)
- Comments in PRs like "per latest feedback"
Fix:
- Better upfront requirements definition
- Earlier product involvement (review designs, not just code)
- Smaller increments with faster feedback loops
Cause 2: Architectural Misalignment
Developer builds a feature using Approach A. Architect or senior engineer reviews and says "this should use Approach B." Developer rebuilds.
Again, not a skill problem—a process problem. The architectural guidance came too late.
Symptoms:
- Churn concentrated in newer team members' work
- Large refactors following code review
- Comments like "refactoring per review"
Fix:
- Architecture review before coding starts
- Better documentation of patterns and conventions
- Pairing junior developers with seniors for complex work
Cause 3: Unstable APIs and Dependencies
Developer builds against API v1. API team ships breaking changes in v2. Developer has to update their code.
The churn appears in the dependent code, but the cause is upstream instability.
Symptoms:
- Churn follows releases from other teams
- Multiple developers affected by same churn cause
- Churn in integration code (API clients, data layers)
Fix:
- API versioning and deprecation policies
- Better coordination between teams
- Integration tests that catch breaking changes early
Cause 4: Legitimate Iteration
Sometimes churn is actually healthy. You build a prototype, learn from it, and refine. Early-stage features naturally churn as the team discovers what works.
Symptoms:
- Churn concentrated in new/experimental features
- Churn decreases as feature matures
- Accompanied by product learning ("we discovered users actually wanted X")
Fix:
- Nothing—this is working as intended
- Maybe set expectations that early features will have high churn
- Don't measure early-stage work by churn standards
Cause 5: Technical Debt Paydown
A file has accumulated debt. A developer touches it and fixes issues along the way. The churn reflects cleanup, not problems.
Symptoms:
- Churn in files with known debt
- Decreasing complexity metrics post-churn
- Developer comments indicating intentional refactoring
Fix:
- Nothing—this is desirable
- Track debt paydown separately from problem churn
Cause 6: Actual Quality Issues
Sometimes, yes, code is simply buggy and needs fixing. But this is less common than managers assume.
Symptoms:
- Churn follows bug reports
- Concentrated in complex logic areas
- Same developer consistently churning same areas
Fix:
- Better test coverage
- Code review focus on the churning areas
- Possible training or pairing for developer
Diagnosing Churn
When you see high churn, don't jump to conclusions. Investigate:
Step 1: Where is the churn?
- Which files/modules have highest churn?
- Which developers are associated?
- Which time periods?
Step 2: What triggered the changes?
- Link churned code to Jira tickets
- Read PR descriptions and comments
- Talk to the developers involved
Step 3: Categorize the cause
- Requirements change?
- Architecture feedback?
- Dependency instability?
- Bug fixing?
- Deliberate refactoring?
Step 4: Address the root cause
- If requirements: Improve product-engineering collaboration
- If architecture: Earlier design review
- If dependencies: Better API contracts
- If bugs: More testing, better review
Churn as Leading Indicator
Churn can predict future problems:
High Churn Files
Files with consistently high churn are often problematic:
- Unclear ownership
- Poor abstraction (everything touches this file)
- Accumulated complexity
These files deserve attention: refactoring, documentation, or architectural redesign.
Churn Correlation with Bugs
Track whether high-churn files also have high bug rates. If so, the churn may indicate instability that's reaching production.
Team Churn Trends
A team's churn rate increasing over time might indicate:
- Growing technical debt
- Deteriorating requirements process
- Architectural problems accumulating
Investigate before the trend becomes a crisis.
What Good Churn Looks Like
Some churn is healthy. Targets depend on context, but generally:
- Mature products: 10-15% rework rate
- Active development: 15-25% rework rate
- New/experimental features: 25-40% rework rate (expected to decrease)
Zero churn is suspicious—it might mean developers aren't iterating or responding to feedback.
Very high churn (>40%) sustained over time indicates a systemic problem worth investigating.
The Conversation
When discussing churn with your team:
Don't say: "Your churn rate is too high. Write better code."
Do say: "I see high churn in module X. Help me understand what's driving it—are requirements changing? Are we getting architectural feedback late? Something else?"
Approach churn as a diagnostic, not a judgment. The data tells you something is happening; the investigation tells you what.
