Rework Rate
Rework Rate measures the proportion of engineering effort spent on revisiting or correcting work that was previously considered complete. It reflects how much delivery capacity is consumed by quality issues, unclear requirements, or inefficient workflows.
Calculation
Rework is typically defined as time spent:
- Fixing bugs found shortly after release
- Revising code based on review feedback or missed requirements
- Refactoring work submitted and rejected during QA or deployment
Rework may be tagged directly or inferred based on linked tickets and timing patterns.
The metric is calculated as:
rework rate = time spent on rework ÷ total engineering time × 100
Goals
Rework Rate helps teams identify where time is being lost due to preventable issues. It answers questions like:
- How much effort is going toward fixing rather than building?
- Are unclear requirements or poor validation driving inefficiencies?
- Are certain teams, services, or processes generating more rework than others?
The metric supports investment decisions in test coverage, review rigor, and planning quality.
Variations
Rework Rate may also be referred to as Engineering Waste Rate, Redo Ratio, or Time Lost to Fixes. Common breakdowns include:
- By rework type, such as bug fix, missed acceptance criteria, or refactor
- By ticket or PR history, using linked work items
- By team, system, or epic, to highlight areas generating high rework
- By timeframe, to track spikes following releases or refactors
Some teams use Merge Rework Rate as a variation that focuses specifically on rework required within pull request workflows.
Limitations
This metric highlights waste, but not root cause or severity. Not all rework is bad some may reflect healthy iteration or proactive improvements. Classification also depends on consistent tagging or heuristic inference.
To better contextualize rework, pair this metric with:
| Complementary Metric | Why It’s Relevant |
|---|---|
| Time Spent on Bugs | Highlights how much rework is tied to defect remediation |
| Change Failure Rate | Shows whether deployments are producing issues that drive rework cycles |
| First-Time Pass Rate | Indicates how often code changes succeed on initial validation without revision |
Optimization
Reducing Rework Rate requires improving planning accuracy, validation, and delivery discipline.
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Strengthen definitions of ready and done. Ensure work is clearly scoped and meets expectations before marking it complete.
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Invest in test coverage and automation. Catch issues earlier to reduce the need for reactive fixes.
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Improve design and review processes. Early feedback during design and coding can prevent later rework.
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Segment and analyze rework sources. Use ticket metadata to identify patterns in what causes the most rework.
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Track and revisit rework trends. Use retrospectives to surface silent debt and prevent it from recurring.
Rework Rate is one of the clearest signals of friction in engineering. When reduced thoughtfully, it frees teams to spend more time building and less time fixing.
