test-reporting-analytics

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

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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.