Quick Risk Assessment:

• Probability factors: Complexity, change frequency, developer experience, technical debt
• Impact factors: User count, revenue, safety, reputation, regulatory
• Dynamic adjustment: Production bugs increase risk; stable code decreases

Critical Success Factors:

• Test where bugs hurt most, not everywhere equally
• Risk is dynamic - reassess with new information
• Production data informs risk (shift-right feeds shift-left)

Quick Reference Card

When to Use

• Planning sprint/release test strategy
• Deciding what to automate first
• Allocating limited testing time
• Justifying test coverage decisions

Risk Calculation

Risk Score = Probability (1-5) × Impact (1-5)

Probability Factors

Impact Factors

Risk Assessment Workflow

Step 1: List Features/Components

Feature | Probability | Impact | Risk | Priority
--------|-------------|--------|------|----------
Checkout | 4 | 5 | 20 | Critical
User Auth | 3 | 5 | 15 | High
Admin Panel | 2 | 2 | 4 | Low
Search | 3 | 3 | 9 | Medium

Step 2: Apply Test Depth

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");

Step 3: Reassess Dynamically

// 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");

ML-Enhanced Risk Analysis

// 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

Agent Coordination Hints

Memory Namespace

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

Fleet Coordination

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'
});

Integration with CI/CD

# 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

Related Skills

• agentic-quality-engineering - Risk-aware agents
• context-driven-testing - Context affects risk
• regression-testing - Risk-based regression selection
• shift-right-testing - Production informs risk

Remember

Risk = Probability × Impact. Test where bugs hurt most. Critical gets 60%, low gets 5%. Risk is dynamic - reassess with new info. Production incidents raise risk scores.

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.