| name | quality-metrics |
| description | Test coverage, code quality, defect metrics, and QA KPIs |
Quality Metrics
Test Coverage Metrics
Code Coverage Types
- Line Coverage: Percentage of executable lines executed
- Branch Coverage: Percentage of conditional branches taken
- Function Coverage: Percentage of functions/methods called
- Statement Coverage: Percentage of statements executed
- Condition Coverage: Percentage of boolean sub-conditions evaluated
Coverage Targets
Critical Business Logic: 90-100%
Core Application Code: 80-90%
Utility/Helper Code: 70-80%
Configuration/Setup Code: 50-70%
Coverage Tools
- JavaScript: Istanbul, nyc, Jest coverage
- Python: pytest-cov, coverage.py
- Java: JaCoCo, Cobertura
- .NET: dotCover, Coverlet
- Go: go test -cover
Coverage Best Practices
- Focus on meaningful coverage, not just percentage
- Prioritize coverage of critical paths
- Track coverage trends over time
- Set coverage gates in CI/CD
- Review uncovered code regularly
Code Quality Metrics
Cyclomatic Complexity
- Measures code complexity based on control flow
- Higher complexity = harder to test and maintain
- Target: < 10 per function/method
Complexity Levels:
1-10: Simple, low risk
11-20: Moderate complexity, medium risk
21-50: High complexity, high risk
50+: Very high complexity, very high risk
Maintainability Index
- Composite metric combining complexity, volume, and structure
- Scale: 0-100 (higher is better)
- Target: > 70
Maintainability Levels:
85-100: Highly maintainable
70-84: Moderately maintainable
50-69: Difficult to maintain
0-49: Very difficult to maintain
Code Duplication
- Percentage of duplicated code
- Target: < 5%
- High duplication indicates need for refactoring
Code Smells
- Long Methods: Methods > 50 lines
- Large Classes: Classes > 500 lines
- Deep Nesting: Nesting > 4 levels
- Long Parameter Lists: > 4 parameters
- Feature Envy: Methods using other objects more than their own
Technical Debt Ratio
Technical Debt Ratio = Cost to Fix Issues / Cost to Develop New Features
Target: < 5%
Defect Metrics
Defect Density
- Defects per thousand lines of code (KLOC)
- Defects per function point
- Defects per module/component
Defect Density = Total Defects / Size Metric (KLOC, FP)
Target: < 1 defect per KLOC
Defect Removal Efficiency (DRE)
- Percentage of defects found before release
- Higher is better
DRE = (Defects Found Before Release / Total Defects) × 100
Target: > 90%
Defect Leakage
- Percentage of defects found after release
- Lower is better
Defect Leakage = (Defects Found After Release / Total Defects) × 100
Target: < 10%
Defect Age
- Time from defect introduction to detection
- Time from defect detection to resolution
Defect Age = Detection Date - Introduction Date
Resolution Time = Resolution Date - Detection Date
Target: < 7 days for critical defects
Defect Severity Distribution
- Critical: System failure, data loss
- High: Major functionality broken
- Medium: Minor functionality broken
- Low: Cosmetic issues, typos
Defect Trend Analysis
- Track defects over time
- Identify patterns and trends
- Correlate with code changes
- Predict future defect rates
Quality Gates and Acceptance Criteria
Quality Gate Examples
Code Quality Gate:
- Code coverage ≥ 80%
- No critical or high severity defects
- Cyclomatic complexity ≤ 10
- No security vulnerabilities
- All tests passing
Performance Gate:
- p95 response time < 500ms
- Error rate < 0.1%
- Throughput ≥ 1000 RPS
- CPU utilization < 70%
Security Gate:
- No high/critical vulnerabilities
- OWASP Top 10 compliance
- Dependency scan clean
- Authentication/authorization tested
Acceptance Criteria
- Functional Requirements: All features work as specified
- Non-Functional Requirements: Performance, security, usability met
- Test Coverage: Minimum coverage thresholds met
- Defect Standards: No defects above acceptable severity
- Documentation: All documentation complete and accurate
Test Reporting and Dashboards
Test Execution Reports
- Total tests run
- Pass/fail counts and percentages
- Test execution time
- Flaky test identification
- Test failure categorization
Coverage Reports
- Line, branch, function coverage
- Coverage by module/component
- Uncovered code highlighting
- Coverage trends over time
- Coverage comparison between branches
Defect Reports
- Open defects by severity
- Defect trends and patterns
- Defect age distribution
- Defect resolution time
- Defect density by module
Performance Reports
- Response time metrics (p50, p95, p99)
- Throughput metrics
- Error rates
- Resource utilization
- Performance trends over time
Dashboard Elements
- Overall quality score
- Test coverage gauge
- Defect trend chart
- Performance metrics
- Build health status
- Quality gate status
QA KPIs and Metrics Tracking
Process Metrics
- Test Execution Time: Time to run test suite
- Test Automation Rate: Percentage of tests automated
- Test Case Count: Total number of test cases
- Test Case Execution: Number of tests executed per period
- Defect Detection Rate: Defects found per testing hour
Product Metrics
- Defect Escape Rate: Defects found in production
- Mean Time to Detection: Time to find defects
- Mean Time to Resolution: Time to fix defects
- Customer Reported Defects: Defects reported by users
- Test Coverage: Code, feature, and requirement coverage
Team Metrics
- Test Case Productivity: Test cases created per person-hour
- Automation Productivity: Automated tests created per person-hour
- Defect Detection Efficiency: Defects found per person-hour
- Test Execution Efficiency: Tests executed per person-hour
Quality Metrics
- Overall Quality Score: Composite quality metric
- Test Pass Rate: Percentage of passing tests
- Defect Density: Defects per size metric
- Code Quality Score: Based on complexity, duplication, smells
- Performance Score: Based on response time, throughput, errors
Metrics Dashboard
Quality Dashboard:
┌─────────────────────────────────────────┐
│ Overall Quality Score: 87/100 │
├─────────────────────────────────────────┤
│ Test Coverage: 85% │
│ ├─ Line: 87% │
│ ├─ Branch: 82% │
│ └─ Function: 90% │
├─────────────────────────────────────────┤
│ Code Quality: 82/100 │
│ ├─ Complexity: 8.2 avg │
│ ├─ Duplication: 3.1% │
│ └─ Maintainability: 78 │
├─────────────────────────────────────────┤
│ Defects: 12 open │
│ ├─ Critical: 1 │
│ ├─ High: 3 │
│ ├─ Medium: 5 │
│ └─ Low: 3 │
├─────────────────────────────────────────┤
│ Performance: 92/100 │
│ ├─ p95 Response: 420ms │
│ ├─ Throughput: 1250 RPS │
│ └─ Error Rate: 0.05% │
└─────────────────────────────────────────┘
Metrics Collection and Analysis
Data Collection
- Automated collection from CI/CD pipelines
- Integration with test frameworks
- Real-time monitoring and alerts
- Historical data storage
- Data normalization and aggregation
Analysis Techniques
- Trend analysis over time
- Comparison between branches/releases
- Correlation with code changes
- Root cause analysis of quality issues
- Predictive analytics for quality forecasting
Reporting Frequency
- Real-time: Build status, test failures, critical defects
- Daily: Test execution, defect updates, performance metrics
- Weekly: Quality trends, coverage reports, team metrics
- Monthly: Quality scorecards, process improvements, strategic insights
- Quarterly: Quality reviews, goal setting, process optimization
Continuous Improvement
- Identify quality trends and patterns
- Set quality goals and targets
- Track progress toward goals
- Implement process improvements based on metrics
- Celebrate quality achievements
