qe-risk-based-testing

# Risk-Based Testing

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When planning tests or allocating testing resources:
1. IDENTIFY risks: What can go wrong? What's the impact? What's the likelihood?
2. CALCULATE risk: Risk = Probability × Impact (use 1-5 scale for each)
3. PRIORITIZE: Critical (20+) → High (12-19) → Medium (6-11) → Low (1-5)
4. ALLOCATE effort: 60% critical, 25% high, 10% medium, 5% low
5. REASSESS continuously: New info, changes, production incidents

**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)
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## 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)
```

| 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 |

### Probability Factors
| Factor | Low (1) | Medium (3) | High (5) |
|--------|---------|------------|----------|
| Complexity | Simple CRUD | Business logic | Algorithms, integrations |
| Change Rate | Stable 6+ months | Monthly changes | Weekly/daily changes |
| Developer Experience | Senior, domain expert | Mid-level | Junior, new to codebase |
| Technical Debt | Clean code | Some debt | Legacy, no tests |

### Impact Factors
| Factor | Low (1) | Medium (3) | High (5) |
|--------|---------|------------|----------|
| Users Affected | Admin only | Department | All users |
| Revenue | None | Indirect | Direct (checkout) |
| Safety | Convenience | Data loss | Physical harm |
| Reputation | Internal | Industry | Public scandal |

---

## 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
```typescript
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
```typescript
// 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

```typescript
// 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
```typescript
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

```yaml
# 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](../agentic-quality-engineering/) - Risk-aware agents
- [context-driven-testing](../context-driven-testing/) - Context affects risk
- [regression-testing](../regression-testing/) - Risk-based regression selection
- [shift-right-testing](../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.