| name | qc-metrics |
| description | Use when measuring and reporting QA quality — defect escape rate, test coverage analysis, flaky test rate, mean time to detect, shift-left metrics, and building quality dashboards for stakeholders. |
QC Metrics & Reporting
When to Use
- Building a QA metrics dashboard for stakeholders
- Evaluating effectiveness of the test strategy
- Demonstrating QA value to leadership
- Identifying where to invest testing effort
- Tracking quality trends over time (improving or degrading?)
Core Jobs
1. Core QA KPIs
Defect Escape Rate (most important)
Formula: Production defects / (Pre-prod defects + Production defects) × 100
Target: < 10% (less than 10% of defects found in production)
Why it matters: Measures testing effectiveness; high rate = gaps in coverage
Test Coverage
Line coverage: % of code lines executed by tests
Branch coverage: % of code branches executed
Requirement coverage: % of requirements with test cases
Target: > 80% branch coverage for business-critical paths
Caution: Coverage is a floor not a ceiling; 100% with bad tests is worthless
Defect Detection Distribution
% defects found per phase: Unit test | Integration | QA | UAT | Production
Target: Most defects found in unit/integration (shift-left)
Cost multiplier: Defect in prod costs ~30x more than unit test phase
Flaky Test Rate
Formula: Tests that failed non-deterministically in last 7 days / total tests
Target: < 2% flaky rate
Impact: High flaky rate erodes trust in test suite, developers start ignoring failures
Mean Time to Detect (MTTD)
Time from bug introduction (code commit) to detection
Target: < 24 hours for critical paths
Enables: Faster feedback, cheaper fixes
Test Execution Time
Time from code commit to test results
Target: < 10 min for unit+integration, < 30 min for full suite
Impact: Slow tests = developers don't run them locally
2. Defect Escape Rate Calculation
def calculate_escape_rate(month: str) -> dict:
pre_prod_defects = get_defects_found_before_production(month)
prod_defects = get_production_incidents(month)
total = len(pre_prod_defects) + len(prod_defects)
escape_rate = len(prod_defects) / total * 100 if total > 0 else 0
return {
"month": month,
"pre_prod_defects": len(pre_prod_defects),
"production_defects": len(prod_defects),
"escape_rate": f"{escape_rate:.1f}%",
"trend": "improving" if escape_rate < get_previous_month_rate() else "degrading"
}
3. Test Pyramid Health Check
Healthy test pyramid:
E2E tests: 5-10% (slow, expensive, cover critical paths)
Integration tests: 20-30% (medium speed, component interactions)
Unit tests: 60-70% (fast, cheap, cover business logic)
Unhealthy patterns:
Ice cream cone (inverted pyramid):
Many E2E, few unit tests → slow CI, brittle suite
Fix: Identify what E2E tests are actually testing unit-level logic
Hourglass (gaps in middle):
Many unit + many E2E, no integration
Fix: Add API/service-level integration tests
Check your pyramid:
grep -r "@pytest.mark" tests/ | grep -c "e2e\|integration\|unit"
4. Quality Dashboard Components
Dashboard sections:
1. Build Health (real-time)
- Last build status: PASS/FAIL
- Build duration trend (line chart)
- Flaky test list (top 10)
2. Coverage Trends (weekly)
- Line coverage % over time (trend line)
- Files with lowest coverage (table)
- Coverage delta per PR
3. Defect Metrics (monthly)
- Defect escape rate over time
- Defect distribution by phase (bar chart)
- Open P1/P2 count (KPI card)
- Time to resolution by priority
4. Release Readiness (per release)
- Test completion % by feature
- Outstanding P1/P2 defects
- Test pass rate last 5 runs
- Sign-off status
5. Shift-Left Metrics
Measuring shift-left effectiveness:
Before shift-left: 70% bugs found in QA/UAT, 30% in unit tests
After 6 months: 50% bugs in unit, 30% integration, 20% QA, <5% production
Leading indicators (predict future quality):
- PR code review turnaround time (faster = fewer bugs pass through)
- Unit test coverage per PR (requires tests with new code)
- Static analysis violations per PR (code quality proxy)
Lagging indicators (measure past quality):
- Defect escape rate (measures testing effectiveness)
- Production incident rate (measures overall quality)
- Customer-reported bugs (ultimate quality measure)
Report: leading indicators weekly (actionable), lagging monthly (strategic)
Key Concepts
- Defect escape rate — % of defects reaching production; primary QA effectiveness metric
- Shift-left — finding defects earlier in development (unit > integration > QA > production)
- Test pyramid — distribution of test types; more unit, fewer E2E
- Flaky test — test that fails non-deterministically; track and quarantine
- Leading vs lagging indicators — leading predict future (coverage), lagging measure past (escape rate)
- MTTD (Mean Time to Detect) — time from bug introduction to discovery
Checklist
Key Outputs
- Monthly QA metrics report (escape rate, coverage, flaky rate, defect distribution)
- Quality dashboard for stakeholder visibility
- Shift-left effectiveness measurement over time
- Recommendations based on metrics (where to invest testing effort)
Output Format
- 🔴 Critical — no defect escape rate tracking (blind to production quality), flaky test rate >10% (suite is unreliable), no coverage measurement
- 🟡 Warning — tracking only line coverage (not branch), no trend analysis (point-in-time only), metrics not visible to stakeholders
- 🟢 Suggestion — build automated weekly metrics email, track coverage delta per PR (not just absolute), add defect phase distribution to understand shift-left progress
Anti-Patterns
- Optimizing for 100% code coverage instead of meaningful tests (vanity metric)
- Reporting only passing tests (ignores flaky tests, skipped tests)
- Metrics with no target (measurement without direction is pointless)
- Only lagging indicators (escape rate tells you after the fact; add leading indicators)
Integration
qc-defect-management — defect data feeds escape rate and distribution metricsado-pipeline-optimization — test result publishing enables coverage trendingtest-strategy — metrics validate and inform the overall test strategy
