6.3Metrics That Actually Matter

Anti-Pattern: Vanity metrics that look good in dashboards but do not drive improvement — automation percentage, total test count, bugs found.

Pattern: Outcome metrics that measure whether testing is achieving its purpose.

Vanity vs Outcome Metrics

Vanity Metric	Why It Misleads	Outcome Metric	Why It Matters
Automation %	90% automation with wrong tests is worse than 50% with right tests	Escaped defects	Bugs that reach production despite testing — the direct measure of test effectiveness
Test count	More tests ≠ better quality; many may be redundant or low-value	MTTR (Mean Time to Recovery)	How fast do you detect and fix production issues?
Bugs found	Finding more bugs can mean worse code, not better testing	Signal-to-noise ratio	% of test failures that are real bugs vs flakiness or environment issues
Pass rate	99% pass rate means nothing if the failing 1% are ignored	Change failure rate	% of deployments that cause a production incident

Behavioral and Trend Signals

Outcome metrics measure the test process. The most senior view also asks whether the product is getting healthier over time and whether users are succeeding:

• System-health trends — regression pass rate, flaky rate, escaped defects by area, defect reopen rate, mean time to detect, coverage by business workflow
• Behavioral signals — top flows and their conversion, highest-abandonment flows, friction pages (rage/dead/error clicks), time-on-task vs baseline, regression coverage mapped to real production flows

"I want QA reporting to tell leadership not just whether today's build passed, but whether the product is becoming more or less stable over time — and whether real users are completing their tasks."