4.4Process Metrics

Cycle Time (for Bug Fixes)

What it measures: The time from when a bug is reported to when the fix is deployed to production.

Formula:

Cycle Time = Deployment Date - Bug Report Date

Breakdown:

Total Cycle Time = Triage Time + Development Time + Testing Time + Deployment Time

Example:
  Bug reported: Monday 9 AM
  Triaged: Monday 2 PM (5 hours)
  Fix developed: Tuesday 4 PM (1 day + 2 hours)
  Fix tested: Wednesday 11 AM (0.5 days)
  Fix deployed: Wednesday 3 PM (4 hours)
  Total Cycle Time: 2.25 business days

Why it matters: Long cycle times for critical bugs mean customers suffer longer. Tracking this metric identifies bottlenecks in the fix-verify-deploy pipeline.

Lead Time (for Features)

What it measures: The time from when a feature is committed to code until it is deployed to production.

Formula:

Lead Time = Deployment Date - First Commit Date

Why it matters for QA: If lead time is long, testing is often the bottleneck. Tracking this metric lets you determine what percentage of lead time is spent in testing and whether that percentage is improving.

Deployment Frequency

What it measures: How often the team deploys to production.

Why it matters for QA: Higher deployment frequency requires faster testing. If the team wants to deploy daily, the regression suite must run in under an hour, not 3 days.

Deployment Frequency	QA Implication
Monthly	Full manual regression is feasible
Weekly	Automated regression required, manual exploratory
Daily	Full automation, feature flags, canary releases
Multiple times per day	Automated everything, production monitoring as testing