Imported from proffesor-for-testing/agentic-qe (.claude/skills/test-reporting-analytics/SKILL.md). Install upstream with npx skills add proffesor-for-testing/agentic-qe --skill test-reporting-analytics. Copyright stays with the author.

Test Reporting & Analytics

<default_to_action> When building test reports:

1. DEFINE audience (dev team vs executives)
2. CHOOSE key metrics (max 5-7)
3. SHOW trends (not just snapshots)
4. HIGHLIGHT actions (what to do about it)
5. AUTOMATE generation

Dashboard Quick Setup:

+------------------+------------------+------------------+
| Tests Passed     | Code Coverage    | Flaky Tests      |
| 1,247/1,250 ✅   | 82.3% ⬆️ +2.1%  | 1.2% ⬇️ -0.3%   |
+------------------+------------------+------------------+
| Critical Bugs    | Deploy Freq      | MTTR             |
| 0 open ✅        | 12x/day ⬆️       | 2.3h ⬇️          |
+------------------+------------------+------------------+

Key Metrics by Audience:

• Dev Team: Pass rate, flaky %, execution time, coverage gaps
• QE Team: Defect detection rate, test velocity, automation ROI
• Leadership: Escaped defects, deployment frequency, quality cost </default_to_action>

Quick Reference Card

Essential Metrics

Trend Indicators

Report Types

Real-Time Dashboard

Live quality status for CI/CD
- Build status (green/red)
- Test results (pass/fail counts)
- Coverage delta
- Flaky test alerts

Sprint Summary

## Sprint 47 Quality Summary

### Metrics
| Metric | Value | Trend |
|--------|-------|-------|
| Tests Added | +47 | ⬆️ |
| Coverage | 82.3% | ⬆️ +2.1% |
| Bugs Found | 12 | ➡️ |
| Escaped | 0 | ✅ |

### Highlights
- ✅ Zero escaped defects
- ⚠️ E2E suite now 45min (target: 30min)

### Actions
1. Optimize slow E2E tests
2. Add coverage for payment module

Executive Report

## Monthly Quality Report - Oct 2025

### Executive Summary
✅ Production uptime: 99.97% (target: 99.95%)
✅ Deploy frequency: 12x/day (up from 8x)
⚠️ Coverage: 82.3% (target: 85%)

### Business Impact
- Automation saves 120 hrs/month
- Bug cost: $150/bug found vs $5,000 escaped
- Estimated annual savings: $450K

### Recommendations
1. Invest in performance testing tooling
2. Hire senior QE for mobile coverage

Predictive Analytics

// Predict test failures
const prediction = await Task("Predict Failures", {
  codeChanges: prDiff,
  historicalData: last90Days,
  model: 'gradient-boosting'
}, "qe-quality-analyzer");

// Returns:
// {
//   failureProbability: 0.73,
//   likelyFailingTests: ['payment.test.ts'],
//   suggestedAction: 'Review payment module carefully',
//   confidence: 0.89
// }

// Trend analysis with anomaly detection
const trends = await Task("Analyze Trends", {
  metrics: ['passRate', 'coverage', 'flakyRate'],
  period: '30d',
  detectAnomalies: true
}, "qe-quality-analyzer");

Agent Integration

// Generate comprehensive quality report
const report = await Task("Generate Quality Report", {
  period: 'sprint',
  audience: 'executive',
  includeROI: true,
  includeTrends: true
}, "qe-quality-analyzer");

// Real-time quality gate check
const gateResult = await Task("Quality Gate Check", {
  metrics: currentMetrics,
  thresholds: qualityPolicy,
  environment: 'production'
}, "qe-quality-gate");

Agent Coordination Hints

Memory Namespace

aqe/reporting/
├── dashboards/*      - Dashboard configurations
├── reports/*         - Generated reports
├── trends/*          - Trend analysis data
└── predictions/*     - Predictive model outputs

Fleet Coordination

const reportingFleet = await FleetManager.coordinate({
  strategy: 'quality-reporting',
  agents: [
    'qe-quality-analyzer',      // Metrics aggregation
    'qe-quality-gate',          // Threshold validation
    'qe-deployment-readiness'   // Release readiness
  ],
  topology: 'parallel'
});

Related Skills

• quality-metrics - Metric definitions
• shift-right-testing - Production metrics
• consultancy-practices - Client reporting

Remember

Measure to improve. Report to communicate.

Good reports:

• Answer "so what?" (actionable insights)
• Show trends (not just snapshots)
• Match audience needs
• Automate where possible

Data without action is noise. Action without data is guessing.