Focus testing effort on highest-risk areas using risk assessment and prioritization. Use when planning test strategy, allocating testing resources, or making coverage decisions.
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<default_to_action> When planning tests or allocating testing resources:
| Score | Priority | Effort | Action |
|---|---|---|---|
| 20-25 | Critical | 60% | Comprehensive testing, multiple techniques |
| 12-19 | High | 25% | Thorough testing, automation priority |
| 6-11 | Medium | 10% | Standard testing, basic automation |
| 1-5 | Low | 5% | Smoke test, exploratory only |
await Task("Risk-Based Test Generation", {
critical: {
features: ['checkout', 'payment'],
depth: 'comprehensive',
techniques: ['unit', 'integration', 'e2e', 'performance', 'security']
},
high: {
features: ['auth', 'user-profile'],
depth: 'thorough',
techniques: ['unit', 'integration', 'e2e']
},
medium: {
features: ['search', 'notifications'],
depth: 'standard',
techniques: ['unit', 'integration']
},
low: {
features: ['admin-panel', 'settings'],
depth: 'smoke',
techniques: ['smoke-tests']
}
}, "qe-test-generator");
// Production incident increases risk
await Task("Update Risk Score", {
feature: 'search',
event: 'production-incident',
previousRisk: 9,
newProbability: 5, // Increased due to incident
newRisk: 15 // Now HIGH priority
}, "qe-regression-risk-analyzer");
// Agent predicts risk using historical data
const riskAnalysis = await Task("ML Risk Analysis", {
codeChanges: changedFiles,
historicalBugs: bugDatabase,
prediction: {
model: 'gradient-boosting',
factors: ['complexity', 'change-frequency', 'author-experience', 'file-age']
}
}, "qe-regression-risk-analyzer");
// Output: 95% accuracy risk prediction per file
aqe/risk-based/
├── risk-scores/* - Current risk assessments
├── historical-bugs/* - Bug patterns by area
├── production-data/* - Incident data for risk
└── coverage-map/* - Test depth by risk level
const riskFleet = await FleetManager.coordinate({
strategy: 'risk-based-testing',
agents: [
'qe-regression-risk-analyzer', // Risk scoring
'qe-test-generator', // Risk-appropriate tests
'qe-production-intelligence', // Production feedback
'qe-quality-gate' // Risk-based gates
],
topology: 'sequential'
});
# Risk-based test selection in pipeline
- name: Risk Analysis
run: aqe risk-analyze --changes ${{ github.event.pull_request.files }}
- name: Run Critical Tests
if: risk.critical > 0
run: npm run test:critical
- name: Run High Tests
if: risk.high > 0
run: npm run test:high
- name: Skip Low Risk
if: risk.low_only
run: npm run test:smoke
With Agents: Agents calculate risk using ML on historical data, select risk-appropriate tests, and adjust scores from production feedback. Use agents to maintain dynamic risk profiles at scale.
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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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