7.8Continuous Improvement: Using Metrics to Drive Process Changes

The Metrics-Driven Improvement Cycle

1. MEASURE    → Collect baseline metrics for 3 months
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2. ANALYZE    → Identify the worst metric (biggest gap to target)
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3. HYPOTHESIZE → "If we do X, metric Y will improve because Z"
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4. EXPERIMENT → Implement the change for 2-4 sprints
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5. EVALUATE   → Did the metric improve? By how much?
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6. DECIDE     → Keep the change, modify it, or revert it
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   Back to 1 (with updated baseline)

Real-World Improvement Examples

Metric Problem	Hypothesis	Experiment	Result
Escaped defect rate: 12%	"Three amigos sessions will catch requirements bugs earlier"	Started three amigos for all high-risk stories	Escaped rate dropped to 6% in 3 sprints
Bug fix cycle time: 5 days	"Bugs are waiting in triage too long"	Implemented daily bug triage (15 min)	Cycle time dropped to 2.5 days
Flaky test rate: 8%	"Most flakiness is from test data dependencies"	Switched to test data factories from static fixtures	Flaky rate dropped to 3%
Automation ratio: 40%	"Developers will write more tests if we provide patterns"	Created test template library and pairing sessions	Ratio increased to 58% in 2 quarters

When Metrics Do Not Improve

If a process change does not improve the target metric after 3-4 sprints:

1. Verify the data. Is the metric being collected correctly?
2. Check the hypothesis. Was the root cause analysis correct?
3. Check the execution. Was the change actually implemented consistently?
4. Consider confounding factors. Did something else change that offset the improvement?
5. Revert and try something different. Sunk cost should not keep you on a failing experiment.