# Quality Metrics Tracking Task

## Purpose
Define, collect, analyze, and track comprehensive quality metrics across all development activities. This task establishes a data-driven approach to quality improvement and provides visibility into quality trends and patterns.

## Integration with Memory System
- **What patterns to search for**: Metric trend patterns, quality improvement correlations, threshold violations, anomaly patterns
- **What outcomes to track**: Quality improvement rates, metric stability, alert effectiveness, action item completion
- **What learnings to capture**: Effective metric thresholds, leading indicators, improvement strategies, metric correlations

## Quality Metrics Categories

### Code Quality Metrics
```yaml
code_quality_metrics:
  static_analysis:
    - complexity: Cyclomatic complexity per function
    - duplication: Code duplication percentage
    - maintainability: Maintainability index
    - technical_debt: Debt ratio and time
  
  dynamic_analysis:
    - test_coverage: Line, branch, function coverage
    - mutation_score: Test effectiveness
    - performance: Response times, resource usage
    - reliability: Error rates, crash frequency
```

### Process Quality Metrics
- [ ] **Development Velocity**: Story points completed
- [ ] **Defect Density**: Defects per KLOC
- [ ] **Lead Time**: Idea to production time
- [ ] **Cycle Time**: Development start to done
- [ ] **Review Efficiency**: Review time and effectiveness

### Product Quality Metrics
- [ ] **User Satisfaction**: NPS, CSAT scores
- [ ] **Defect Escape Rate**: Production bugs
- [ ] **Mean Time to Recovery**: Incident resolution
- [ ] **Feature Adoption**: Usage analytics
- [ ] **Performance SLAs**: Uptime, response times

## Metric Collection Framework

### Step 1: Automated Collection
```python
def collect_quality_metrics():
    metrics = {
        "code": {
            "coverage": get_test_coverage(),
            "complexity": calculate_complexity(),
            "duplication": detect_duplication(),
            "violations": count_lint_violations()
        },
        "process": {
            "velocity": calculate_velocity(),
            "lead_time": measure_lead_time(),
            "review_time": average_review_time(),
            "build_success": build_success_rate()
        },
        "product": {
            "availability": calculate_uptime(),
            "performance": measure_response_times(),
            "errors": count_error_rates(),
            "satisfaction": get_user_scores()
        }
    }
    return enrich_with_trends(metrics)
```

### Step 2: Metric Analysis
```python
def analyze_metrics(current_metrics, historical_data):
    analysis = {
        "trends": calculate_trends(current_metrics, historical_data),
        "anomalies": detect_anomalies(current_metrics),
        "correlations": find_correlations(current_metrics),
        "predictions": forecast_trends(historical_data),
        "health_score": calculate_overall_health(current_metrics)
    }
    
    return generate_insights(analysis)
```

### Step 3: Threshold Management
| Metric | Green | Yellow | Red | Action |
|--------|-------|---------|-----|---------|
| Test Coverage | >90% | 80-90% | <80% | Block deployment |
| Complexity | <10 | 10-20 | >20 | Refactor required |
| Build Success | >95% | 85-95% | <85% | Fix immediately |
| Review Time | <4hr | 4-8hr | >8hr | Escalate |
| Error Rate | <0.1% | 0.1-1% | >1% | Incident response |

## Quality Dashboard Design

### Real-Time Metrics
```yaml
realtime_dashboard:
  current_sprint:
    - velocity_burndown: Actual vs planned
    - quality_gates: Pass/fail status
    - defect_trend: New vs resolved
    - coverage_delta: Change from baseline
  
  system_health:
    - error_rate: Last 15 minutes
    - response_time: P50, P95, P99
    - availability: Current status
    - active_incidents: Count and severity
```

### Historical Analytics
```python
historical_views = {
    "quality_trends": {
        "timeframes": ["daily", "weekly", "monthly", "quarterly"],
        "metrics": ["coverage", "complexity", "defects", "velocity"],
        "comparisons": ["period_over_period", "target_vs_actual"]
    },
    "pattern_analysis": {
        "defect_patterns": "Common causes and times",
        "performance_patterns": "Peak usage impacts",
        "team_patterns": "Productivity cycles"
    }
}
```

## Alert and Action Framework

### Alert Configuration
```python
alert_rules = {
    "critical": {
        "coverage_drop": "Coverage decreased >5%",
        "build_failure": "3 consecutive failures",
        "production_error": "Error rate >2%",
        "sla_breach": "Response time >SLA"
    },
    "warning": {
        "trend_negative": "3-day negative trend",
        "threshold_approach": "Within 10% of limit",
        "anomaly_detected": "Outside 2 std deviations"
    }
}

def trigger_alert(metric, severity, value):
    alert = {
        "metric": metric,
        "severity": severity,
        "value": value,
        "threshold": get_threshold(metric),
        "action_required": get_required_action(metric, severity)
    }
    notify_stakeholders(alert)
```

### Action Tracking
```markdown
## Quality Action Item
**Metric**: {metric_name}
**Issue**: {threshold_violation}
**Severity**: {critical/high/medium}
**Detected**: {timestamp}

### Required Actions
1. **Immediate**: {emergency_action}
2. **Short-term**: {fix_action}
3. **Long-term**: {prevention_action}

### Tracking
- **Owner**: {responsible_person}
- **Due Date**: {deadline}
- **Status**: {in_progress/blocked/complete}
```

## Success Criteria
- 100% automated metric collection
- <5 minute data freshness
- Zero manual metric calculation
- 90% alert accuracy (not false positives)
- Action completion rate >95%

## Memory Integration
```python
# Quality metrics memory
quality_metrics_memory = {
    "type": "quality_metrics_snapshot",
    "timestamp": collection_time,
    "metrics": {
        "code_quality": code_metrics,
        "process_quality": process_metrics,
        "product_quality": product_metrics
    },
    "analysis": {
        "trends": identified_trends,
        "anomalies": detected_anomalies,
        "correlations": metric_relationships,
        "health_score": overall_score
    },
    "alerts": {
        "triggered": alerts_sent,
        "false_positives": incorrect_alerts,
        "missed_issues": undetected_problems
    },
    "actions": {
        "created": action_items_created,
        "completed": actions_resolved,
        "effectiveness": improvement_achieved
    },
    "insights": {
        "patterns": recurring_patterns,
        "predictions": forecast_accuracy,
        "recommendations": suggested_improvements
    }
}
```

## Metrics Report Template
```markdown
# Quality Metrics Report
**Period**: {start_date} - {end_date}
**Overall Health**: {score}/100

## Executive Summary
- **Quality Trend**: {improving/stable/declining}
- **Key Achievements**: {top_improvements}
- **Main Concerns**: {top_issues}
- **Action Items**: {count} ({completed}/{total})

## Detailed Metrics

### Code Quality
| Metric | Current | Target | Trend | Status |
|--------|---------|---------|--------|---------|
| Coverage | {n}% | {t}% | {↑↓→} | {🟢🟡🔴} |
| Complexity | {n} | {t} | {↑↓→} | {🟢🟡🔴} |

### Process Quality
| Metric | Current | Target | Trend | Status |
|--------|---------|---------|--------|---------|
| Velocity | {n} | {t} | {↑↓→} | {🟢🟡🔴} |
| Lead Time | {n}d | {t}d | {↑↓→} | {🟢🟡🔴} |

### Product Quality
| Metric | Current | Target | Trend | Status |
|--------|---------|---------|--------|---------|
| Availability | {n}% | {t}% | {↑↓→} | {🟢🟡🔴} |
| Error Rate | {n}% | {t}% | {↑↓→} | {🟢🟡🔴} |

## Insights & Patterns
1. **Finding**: {insight}
   - Impact: {description}
   - Recommendation: {action}

## Action Plan
| Action | Owner | Due Date | Status |
|--------|--------|----------|---------|
| {action} | {owner} | {date} | {status} |

## Next Period Focus
{key_areas_for_improvement}
```

## Brotherhood Collaboration
- Metric definition with all teams
- Threshold setting with stakeholders
- Alert configuration with ops team
- Action planning with leadership