Advanced test reporting, quality dashboards, predictive analytics, trend analysis, and executive reporting for QE metrics. Use when communicating quality status, tracking trends, or making data-driven decisions.
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<default_to_action> When building test reports:
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:
| Category | Metric | Target |
|---|---|---|
| Execution | Pass Rate | >98% |
| Execution | Flaky Test % | <2% |
| Execution | Suite Duration | <10 min |
| Coverage | Line Coverage | >80% |
| Coverage | Branch Coverage | >70% |
| Quality | Escaped Defects | <5/release |
| Quality | MTTR | <4 hours |
| Efficiency | Automation Rate | >90% |
| Symbol | Meaning | Action |
|---|---|---|
| ⬆️ | Improving | Continue current approach |
| ⬇️ | Declining | Investigate root cause |
| ➡️ | Stable | Maintain or improve |
| ⚠️ | Threshold breach | Immediate attention |
Live quality status for CI/CD
- Build status (green/red)
- Test results (pass/fail counts)
- Coverage delta
- Flaky test alerts
## 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
## 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
// 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");
// 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");
aqe/reporting/
├── dashboards/* - Dashboard configurations
├── reports/* - Generated reports
├── trends/* - Trend analysis data
└── predictions/* - Predictive model outputs
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'
});
Measure to improve. Report to communicate.
Good reports:
Data without action is noise. Action without data is guessing.
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Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
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WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use when ensuring legal compliance (ADA, Section 508), testing for disabilities, or building accessible applications for 1 billion disabled users globally.
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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
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