quality-metrics

Measure quality effectively with actionable metrics. Use when establishing quality dashboards, defining KPIs, or evaluating test effectiveness.

About quality-metrics

quality-metrics is a Claude AI skill developed by proffesor-for-testing. Measure quality effectively with actionable metrics. Use when establishing quality dashboards, defining KPIs, or evaluating test effectiveness. This powerful Claude Code plugin helps developers automate workflows and enhance productivity with intelligent AI assistance.

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2025-11-09

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namequality-metrics
descriptionMeasure quality effectively with actionable metrics. Use when establishing quality dashboards, defining KPIs, or evaluating test effectiveness.
categorytesting-methodologies
priorityhigh
tokenEstimate900
agents["qe-quality-analyzer","qe-test-executor","qe-coverage-analyzer","qe-production-intelligence","qe-quality-gate"]
implementation_statusoptimized
optimization_version1
last_optimized"2025-12-02T00:00:00.000Z"
dependencies[]
quick_reference_cardtrue
tags["metrics","dora","quality-gates","dashboards","kpis","measurement"]

Quality Metrics

<default_to_action> When measuring quality or building dashboards:

  1. MEASURE outcomes (bug escape rate, MTTD) not activities (test count)
  2. FOCUS on DORA metrics: Deployment frequency, Lead time, MTTD, MTTR, Change failure rate
  3. AVOID vanity metrics: 100% coverage means nothing if tests don't catch bugs
  4. SET thresholds that drive behavior (quality gates block bad code)
  5. TREND over time: Direction matters more than absolute numbers

Quick Metric Selection:

  • Speed: Deployment frequency, lead time for changes
  • Stability: Change failure rate, MTTR
  • Quality: Bug escape rate, defect density, test effectiveness
  • Process: Code review time, flaky test rate

Critical Success Factors:

  • Metrics without action are theater
  • What you measure is what you optimize
  • Trends matter more than snapshots </default_to_action>

Quick Reference Card

When to Use

  • Building quality dashboards
  • Defining quality gates
  • Evaluating testing effectiveness
  • Justifying quality investments

Meaningful vs Vanity Metrics

✅ Meaningful❌ Vanity
Bug escape rateTest case count
MTTD (detection)Lines of test code
MTTR (recovery)Test executions
Change failure rateCoverage % (alone)
Lead time for changesRequirements traced

DORA Metrics

MetricEliteHighMediumLow
Deploy FrequencyOn-demandWeeklyMonthlyYearly
Lead Time< 1 hour< 1 week< 1 month> 6 months
Change Failure Rate< 5%< 15%< 30%> 45%
MTTR< 1 hour< 1 day< 1 week> 1 month

Quality Gate Thresholds

MetricBlocking ThresholdWarning
Test pass rate100%-
Critical coverage> 80%> 70%
Security critical0-
Performance p95< 200ms< 500ms
Flaky tests< 2%< 5%

Core Metrics

Bug Escape Rate

Bug Escape Rate = (Production Bugs / Total Bugs Found) × 100

Target: < 10% (90% caught before production)

Test Effectiveness

Test Effectiveness = (Bugs Found by Tests / Total Bugs) × 100

Target: > 70%

Defect Density

Defect Density = Defects / KLOC

Good: < 1 defect per KLOC

Mean Time to Detect (MTTD)

MTTD = Time(Bug Reported) - Time(Bug Introduced)

Target: < 1 day for critical, < 1 week for others

Dashboard Design

// Agent generates quality dashboard await Task("Generate Dashboard", { metrics: { delivery: ['deployment-frequency', 'lead-time', 'change-failure-rate'], quality: ['bug-escape-rate', 'test-effectiveness', 'defect-density'], stability: ['mttd', 'mttr', 'availability'], process: ['code-review-time', 'flaky-test-rate', 'coverage-trend'] }, visualization: 'grafana', alerts: { critical: { bug_escape_rate: '>20%', mttr: '>24h' }, warning: { coverage: '<70%', flaky_rate: '>5%' } } }, "qe-quality-analyzer");

Quality Gate Configuration

{ "qualityGates": { "commit": { "coverage": { "min": 80, "blocking": true }, "lint": { "errors": 0, "blocking": true } }, "pr": { "tests": { "pass": "100%", "blocking": true }, "security": { "critical": 0, "blocking": true }, "coverage_delta": { "min": 0, "blocking": false } }, "release": { "e2e": { "pass": "100%", "blocking": true }, "performance_p95": { "max_ms": 200, "blocking": true }, "bug_escape_rate": { "max": "10%", "blocking": false } } } }

Agent-Assisted Metrics

// Calculate quality trends await Task("Quality Trend Analysis", { timeframe: '90d', metrics: ['bug-escape-rate', 'mttd', 'test-effectiveness'], compare: 'previous-90d', predictNext: '30d' }, "qe-quality-analyzer"); // Evaluate quality gate await Task("Quality Gate Evaluation", { buildId: 'build-123', environment: 'staging', metrics: currentMetrics, policy: qualityPolicy }, "qe-quality-gate");

Agent Coordination Hints

Memory Namespace

aqe/quality-metrics/
├── dashboards/*         - Dashboard configurations
├── trends/*             - Historical metric data
├── gates/*              - Gate evaluation results
└── alerts/*             - Triggered alerts

Fleet Coordination

const metricsFleet = await FleetManager.coordinate({ strategy: 'quality-metrics', agents: [ 'qe-quality-analyzer', // Trend analysis 'qe-test-executor', // Test metrics 'qe-coverage-analyzer', // Coverage data 'qe-production-intelligence', // Production metrics 'qe-quality-gate' // Gate decisions ], topology: 'mesh' });

Common Traps

TrapProblemSolution
Coverage worship100% coverage, bugs still escapeMeasure bug escape rate instead
Test count focusMany tests, slow feedbackMeasure execution time
Activity metricsBusy work, no outcomesMeasure outcomes (MTTD, MTTR)
Point-in-timeSnapshot without contextTrack trends over time

Related Skills


Remember

Measure outcomes, not activities. Bug escape rate > test count. MTTD/MTTR > coverage %. Trends > snapshots. Set gates that block bad code. What you measure is what you optimize.

With Agents: Agents track metrics automatically, analyze trends, trigger alerts, and make gate decisions. Use agents to maintain continuous quality visibility.

proffesor-for-testing

proffesor-for-testing

agentic-qe

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