Test Debt Register: Managing Untested Areas and Automation Gaps

test_debt_categories:
  coverage_debt:
    description: "Features or code paths not covered by tests"
    examples:
      - "Legacy modules without unit tests"
      - "Edge cases not covered in test suites"
      - "New features shipped with minimal testing"
    risk_impact: "High - Unknown defects may exist"

  automation_debt:
    description: "Tests that should be automated but are manual"
    examples:
      - "Regression tests executed manually each release"
      - "API tests performed via Postman instead of automated"
      - "Data validation checks done manually"
    risk_impact: "Medium - Inefficiency, human error prone"

  maintenance_debt:
    description: "Existing tests that are outdated or broken"
    examples:
      - "Flaky tests disabled temporarily"
      - "Tests failing due to UI changes"
      - "Outdated test data causing failures"
    risk_impact: "Medium - False confidence, wasted effort"

  tooling_debt:
    description: "Infrastructure gaps limiting test effectiveness"
    examples:
      - "No performance testing framework"
      - "Limited test environment availability"
      - "Insufficient test data generation tools"
    risk_impact: "Medium - Capability limitations"

  documentation_debt:
    description: "Missing or outdated test documentation"
    examples:
      - "Test cases not documented"
      - "API contract tests without specifications"
      - "Exploratory testing without session notes"
    risk_impact: "Low - Knowledge transfer issues"

# Test Debt Register
**Last Updated**: 2025-10-10
**Owner**: QA Lead
**Review Frequency**: Bi-weekly

## Executive Summary
- **Total Debt Items**: 47
- **High Risk Items**: 12
- **Estimated Payoff Effort**: 340 hours
- **Priority for Next Sprint**: 5 items (80 hours)

---

## Debt Item Template

### TD-001: Payment Flow - Refund Scenarios Not Tested
**Category**: Coverage Debt
**Priority**: High
**Risk Score**: 8/10

**Description**:
Refund processing flows (full refund, partial refund, refund to original payment method) have not been tested due to payment gateway test environment limitations.

**Affected Components**:
- `payment-service/refund-processor`
- `order-service/refund-handler`
- Frontend: Refund request UI

**Business Impact**:
- Financial transactions at risk
- Customer satisfaction impact if refunds fail
- Potential regulatory compliance issues

**Technical Impact**:
- Unknown behavior in edge cases
- Error handling not validated
- Data consistency not verified

**Risk Assessment**:
| Factor | Score (1-10) | Weight | Weighted Score |
|--------|--------------|--------|----------------|
| Probability of Failure | 6 | 0.3 | 1.8 |
| Impact if Failure Occurs | 9 | 0.4 | 3.6 |
| Frequency of Use | 7 | 0.2 | 1.4 |
| Detection Difficulty | 8 | 0.1 | 0.8 |
| **Total Risk Score** | - | - | **7.6/10** |

**Temporary Mitigation**:
- Manual testing checklist for refund scenarios
- Extra code review for refund-related changes
- Production monitoring alerts for refund failures

**Payoff Plan**:
- **Effort Estimate**: 40 hours
- **Dependencies**: Payment gateway sandbox access
- **Timeline**: Sprint 24 (Week of Oct 21)
- **Resources Required**: 1 QA Engineer, 1 Payment SME
- **Success Criteria**:
  - All refund scenarios automated
  - Integration tests covering gateway responses
  - Edge cases documented and tested

**Created**: 2025-09-15
**Last Updated**: 2025-10-10
**Status**: Active
**Assignee**: Sarah Chen (QA)

---

### TD-002: E2E Tests - Mobile Responsiveness
**Category**: Automation Debt
**Priority**: Medium
**Risk Score**: 5/10

**Description**:
Mobile responsive behavior is tested manually for each release. No automated visual regression or responsive design tests exist.

**Affected Components**:
- All frontend pages
- Mobile-specific layouts
- Touch interactions

**Business Impact**:
- 40% of users access via mobile
- Poor mobile experience affects conversion
- Competitor advantage if mobile UX degrades

**Payoff Plan**:
- **Effort Estimate**: 24 hours
- **Approach**: Implement visual regression tests using Percy or similar
- **Timeline**: Sprint 25
- **Expected ROI**: Save 8 hours/sprint in manual testing

**Status**: Planned for Q4 2025

---

# risk_calculator.py
class TestDebtRiskCalculator:
    """
    Calculate risk scores for test debt items
    """

    def calculate_risk_score(self, debt_item):
        """
        Calculate weighted risk score based on multiple factors

        Args:
            debt_item: Dictionary with risk factors

        Returns:
            float: Risk score between 0-10
        """
        factors = {
            'probability_of_failure': {
                'score': debt_item.get('probability', 5),
                'weight': 0.3
            },
            'impact_if_failure': {
                'score': debt_item.get('impact', 5),
                'weight': 0.4
            },
            'frequency_of_use': {
                'score': debt_item.get('frequency', 5),
                'weight': 0.2
            },
            'detection_difficulty': {
                'score': debt_item.get('detection', 5),
                'weight': 0.1
            }
        }

        risk_score = sum(
            factor['score'] * factor['weight']
            for factor in factors.values()
        )

        return round(risk_score, 1)

    def prioritize_debt_items(self, debt_register):
        """
        Prioritize test debt items for payoff

        Returns ranked list with payoff recommendations
        """
        scored_items = []

        for item in debt_register:
            risk_score = self.calculate_risk_score(item)
            effort = item.get('effort_hours', 0)

            # Calculate ROI: risk reduction per hour of effort
            roi = risk_score / effort if effort > 0 else 0

            scored_items.append({
                'id': item['id'],
                'description': item['description'],
                'risk_score': risk_score,
                'effort': effort,
                'roi': round(roi, 3),
                'priority': self._calculate_priority(risk_score, effort)
            })

        # Sort by priority (High ROI, High Risk first)
        return sorted(
            scored_items,
            key=lambda x: (x['priority'], -x['roi']),
            reverse=True
        )

    def _calculate_priority(self, risk_score, effort):
        """Determine priority based on risk and effort"""
        if risk_score >= 7 and effort <= 40:
            return 'Critical'  # High risk, quick fix
        elif risk_score >= 7:
            return 'High'  # High risk, significant effort
        elif risk_score >= 5 and effort <= 24:
            return 'Medium'  # Medium risk, reasonable effort
        else:
            return 'Low'

# Example usage
calculator = TestDebtRiskCalculator()

debt_items = [
    {
        'id': 'TD-001',
        'description': 'Payment refund scenarios',
        'probability': 6,
        'impact': 9,
        'frequency': 7,
        'detection': 8,
        'effort_hours': 40
    },
    {
        'id': 'TD-002',
        'description': 'Mobile responsiveness tests',
        'probability': 5,
        'impact': 6,
        'frequency': 8,
        'detection': 4,
        'effort_hours': 24
    }
]

prioritized = calculator.prioritize_debt_items(debt_items)
"""
Output:
[
    {
        'id': 'TD-001',
        'risk_score': 7.6,
        'effort': 40,
        'roi': 0.190,
        'priority': 'High'
    },
    {
        'id': 'TD-002',
        'risk_score': 5.8,
        'effort': 24,
        'roi': 0.242,
        'priority': 'Medium'
    }
]
"""

## Test Debt Risk Heat Map

### High Impact / High Probability (Address Immediately)
- TD-001: Payment refund scenarios (Risk: 7.6)
- TD-003: Authentication edge cases (Risk: 9.0)
- TD-009: Data migration rollback (Risk: 8.2)

### High Impact / Low Probability (Monitor & Plan)
- TD-011: Disaster recovery scenarios (Risk: 6.5)
- TD-015: Multi-region failover (Risk: 6.0)

### Low Impact / High Probability (Quick Wins)
- TD-006: UI text validation (Risk: 4.5)
- TD-018: CSV export formats (Risk: 4.2)

### Low Impact / Low Probability (Defer)
- TD-020: Admin panel edge cases (Risk: 2.8)
- TD-022: Legacy report filters (Risk: 2.5)

**Visual Representation**:

## Automation Maturity Assessment

### Unit Tests
- **Current Coverage**: 78%
- **Target Coverage**: 85%
- **Gap**: 7% (estimated 40 hours to close)
- **Debt Items**: TD-012, TD-014, TD-019

### Integration Tests
- **Current Coverage**: 45%
- **Target Coverage**: 70%
- **Gap**: 25% (estimated 120 hours to close)
- **Debt Items**: TD-001, TD-003, TD-007, TD-013

### E2E Tests
- **Current Coverage**: 60% (critical paths only)
- **Target Coverage**: 80%
- **Gap**: 20% (estimated 80 hours to close)
- **Debt Items**: TD-002, TD-006, TD-010

### Performance Tests
- **Current Coverage**: 10%
- **Target Coverage**: 50%
- **Gap**: 40% (estimated 160 hours to close)
- **Debt Items**: TD-005, TD-016, TD-017

### Total Automation Debt: 400 hours
### Quarterly Reduction Target: 100 hours (25%)

## Debt Reduction Strategies

### Strategy 1: Opportunistic Payoff
When working on a feature, pay off related test debt
- Benefit: No dedicated time needed
- Risk: Incomplete debt reduction

### Strategy 2: Dedicated Sprints
Allocate entire sprint to debt reduction
- Benefit: Focused effort, significant progress
- Risk: Business pressure to skip

### Strategy 3: Continuous 20% Allocation
Reserve 20% of each sprint for debt payoff
- Benefit: Steady progress, sustainable
- Risk: Slower than dedicated sprints

### Strategy 4: Risk-Based Blitz
Target highest risk items in focused effort
- Benefit: Quick risk reduction
- Risk: Low-risk debt accumulates

**Recommended Approach**: Combination
- 15% continuous allocation for medium/low debt
- Quarterly debt blitz for high-risk items
- Opportunistic payoff when touching related code