# TraceRTM Features & Algorithms: Deep Dive

Comprehensive research on specific features, algorithms, and implementations for TraceRTM.

---

## 🎯 CORE FEATURES

### Feature 1: Bidirectional Traceability

**What It Does**:
- Links requirements to tests, code, documentation
- Maintains both forward and backward links
- Ensures consistency in both directions

**Algorithm**:
```python
def create_bidirectional_link(source_id, target_id, link_type):
    """Create bidirectional traceability link"""
    
    # Forward link
    forward_link = Relationship(
        source_id=source_id,
        target_id=target_id,
        relationship_type=link_type
    )
    
    # Reverse link (automatically created)
    reverse_type = get_reverse_type(link_type)  # e.g., "tested_by" -> "tests"
    reverse_link = Relationship(
        source_id=target_id,
        target_id=source_id,
        relationship_type=reverse_type
    )
    
    db.add(forward_link)
    db.add(reverse_link)
    db.commit()
    
    # Trigger view updates
    refresh_views_for_requirements([source_id, target_id])
```

**Performance**: O(1) for creation, O(n) for queries

### Feature 2: Impact Analysis

**What It Does**:
- Shows all requirements affected by a change
- Calculates impact scope
- Identifies critical paths

**Algorithm** (Breadth-First Search):
```python
def calculate_impact(requirement_id, max_depth=10):
    """Calculate impact of changing a requirement"""
    
    from collections import deque
    
    visited = set()
    queue = deque([(requirement_id, 0)])
    impact_chain = []
    
    while queue:
        current_id, depth = queue.popleft()
        
        if current_id in visited or depth > max_depth:
            continue
        
        visited.add(current_id)
        
        # Find all downstream requirements
        downstream = db.query(Relationship).filter(
            Relationship.source_id == current_id
        ).all()
        
        for rel in downstream:
            impact_chain.append({
                'id': rel.target_id,
                'depth': depth + 1,
                'path': get_path(requirement_id, rel.target_id)
            })
            queue.append((rel.target_id, depth + 1))
    
    return {
        'source': requirement_id,
        'affected_count': len(visited) - 1,
        'impact_chain': impact_chain,
        'max_depth': max([i['depth'] for i in impact_chain], default=0)
    }
```

**Complexity**: O(V + E) where V = requirements, E = relationships

### Feature 3: Coverage Analysis

**What It Does**:
- Tracks test coverage for requirements
- Tracks code coverage for requirements
- Identifies gaps

**Algorithm**:
```python
def calculate_coverage(requirement_id):
    """Calculate test and code coverage"""
    
    # Get all test links
    test_links = db.query(Relationship).filter(
        Relationship.source_id == requirement_id,
        Relationship.relationship_type == 'tested_by'
    ).all()
    
    # Get all code links
    code_links = db.query(Relationship).filter(
        Relationship.source_id == requirement_id,
        Relationship.relationship_type == 'implemented_by'
    ).all()
    
    test_coverage = len(test_links) > 0
    code_coverage = len(code_links) > 0
    
    return {
        'requirement_id': requirement_id,
        'test_coverage': test_coverage,
        'test_count': len(test_links),
        'code_coverage': code_coverage,
        'code_count': len(code_links),
        'coverage_status': 'Covered' if (test_coverage and code_coverage) else 'Partial',
        'coverage_percentage': (int(test_coverage) + int(code_coverage)) / 2 * 100
    }
```

**Complexity**: O(n) where n = relationships

### Feature 4: Traceability Matrix Generation

**What It Does**:
- Generates matrix showing all relationships
- Supports filtering and sorting
- Exports to various formats

**Algorithm**:
```python
def generate_traceability_matrix(filters=None):
    """Generate traceability matrix"""
    
    query = db.query(Requirement).all()
    
    # Apply filters
    if filters:
        if 'status' in filters:
            query = [r for r in query if r.status == filters['status']]
        if 'type' in filters:
            query = [r for r in query if r.type == filters['type']]
    
    matrix = []
    for req in query:
        row = {
            'id': req.id,
            'title': req.title,
            'status': req.status,
            'tests': get_linked_items(req.id, 'tested_by'),
            'code': get_linked_items(req.id, 'implemented_by'),
            'depends_on': get_linked_items(req.id, 'depends_on'),
            'blocks': get_linked_items(req.id, 'blocks'),
        }
        matrix.append(row)
    
    return matrix
```

**Complexity**: O(n * m) where n = requirements, m = avg relationships

### Feature 5: Cycle Detection

**What It Does**:
- Detects circular dependencies
- Prevents invalid relationships
- Warns about potential cycles

**Algorithm** (DFS-based):
```python
def detect_cycles(source_id, target_id):
    """Detect if adding link would create cycle"""
    
    visited = set()
    rec_stack = set()
    
    def has_cycle(node):
        visited.add(node)
        rec_stack.add(node)
        
        # Get all outgoing edges
        neighbors = db.query(Relationship).filter(
            Relationship.source_id == node
        ).all()
        
        for rel in neighbors:
            neighbor = rel.target_id
            
            if neighbor == source_id:  # Would create cycle
                return True
            
            if neighbor not in visited:
                if has_cycle(neighbor):
                    return True
            elif neighbor in rec_stack:
                return True
        
        rec_stack.remove(node)
        return False
    
    return has_cycle(target_id)
```

**Complexity**: O(V + E)

### Feature 6: Shortest Path Analysis

**What It Does**:
- Finds shortest path between requirements
- Useful for understanding relationships
- Helps with impact analysis

**Algorithm** (Dijkstra's):
```python
def find_shortest_path(source_id, target_id):
    """Find shortest path between requirements"""
    
    import heapq
    
    distances = {source_id: 0}
    previous = {}
    pq = [(0, source_id)]
    visited = set()
    
    while pq:
        current_dist, current = heapq.heappop(pq)
        
        if current in visited:
            continue
        
        visited.add(current)
        
        if current == target_id:
            # Reconstruct path
            path = []
            node = target_id
            while node in previous:
                path.append(node)
                node = previous[node]
            path.append(source_id)
            return list(reversed(path))
        
        # Get neighbors
        neighbors = db.query(Relationship).filter(
            Relationship.source_id == current
        ).all()
        
        for rel in neighbors:
            neighbor = rel.target_id
            new_dist = current_dist + 1
            
            if neighbor not in distances or new_dist < distances[neighbor]:
                distances[neighbor] = new_dist
                previous[neighbor] = current
                heapq.heappush(pq, (new_dist, neighbor))
    
    return None  # No path found
```

**Complexity**: O((V + E) log V)

### Feature 7: Critical Path Analysis

**What It Does**:
- Identifies critical path in dependency graph
- Shows longest path through dependencies
- Helps with project planning

**Algorithm**:
```python
def find_critical_path():
    """Find critical path in dependency graph"""
    
    # Topological sort
    topo_order = topological_sort()
    
    # Calculate longest path
    longest_paths = {}
    
    for node in topo_order:
        longest_paths[node] = 1
        
        # Get all incoming edges
        incoming = db.query(Relationship).filter(
            Relationship.target_id == node
        ).all()
        
        for rel in incoming:
            source = rel.source_id
            longest_paths[node] = max(
                longest_paths[node],
                longest_paths.get(source, 0) + 1
            )
    
    # Find critical path
    max_length = max(longest_paths.values())
    critical_nodes = [n for n, l in longest_paths.items() if l == max_length]
    
    return {
        'critical_path_length': max_length,
        'critical_nodes': critical_nodes,
        'critical_path': reconstruct_critical_path(critical_nodes)
    }
```

**Complexity**: O(V + E)

---

## 🔍 QUERY OPTIMIZATION TECHNIQUES

### Technique 1: Query Caching

```python
from functools import lru_cache
import time

class CachedQuery:
    def __init__(self, ttl_seconds=300):
        self.cache = {}
        self.ttl = ttl_seconds
    
    def get(self, key, query_func):
        if key in self.cache:
            value, timestamp = self.cache[key]
            if time.time() - timestamp < self.ttl:
                return value
        
        value = query_func()
        self.cache[key] = (value, time.time())
        return value
    
    def invalidate(self, key):
        if key in self.cache:
            del self.cache[key]

# Usage
cache = CachedQuery(ttl_seconds=300)
impact = cache.get(f'impact_{req_id}', 
                   lambda: calculate_impact(req_id))
```

### Technique 2: Batch Operations

```python
def batch_update_coverage(requirement_ids):
    """Update coverage for multiple requirements"""
    
    # Single query instead of N queries
    coverage_data = db.query(
        Requirement.id,
        func.count(Relationship.id).label('link_count')
    ).outerjoin(Relationship).filter(
        Requirement.id.in_(requirement_ids)
    ).group_by(Requirement.id).all()
    
    return {req_id: count for req_id, count in coverage_data}
```

### Technique 3: Materialized View Indexing

```sql
-- Create indexes on materialized views
CREATE INDEX idx_traceability_source ON traceability_matrix(source_id);
CREATE INDEX idx_traceability_target ON traceability_matrix(target_id);
CREATE INDEX idx_traceability_type ON traceability_matrix(relationship_type);

-- Composite index for common queries
CREATE INDEX idx_traceability_composite 
ON traceability_matrix(source_id, relationship_type);
```

---

## 📊 ALGORITHM COMPLEXITY ANALYSIS

| Algorithm | Time | Space | Use Case |
|-----------|------|-------|----------|
| Bidirectional Link | O(1) | O(1) | Create link |
| Impact Analysis | O(V+E) | O(V) | Change impact |
| Coverage Analysis | O(n) | O(n) | Coverage metrics |
| Cycle Detection | O(V+E) | O(V) | Validate links |
| Shortest Path | O((V+E)logV) | O(V) | Path analysis |
| Critical Path | O(V+E) | O(V) | Project planning |
| Traceability Matrix | O(n*m) | O(n*m) | Report generation |

---

## 🚀 IMPLEMENTATION PRIORITIES

### Phase 1: Core Algorithms (Week 1)
- ✅ Bidirectional traceability
- ✅ Impact analysis
- ✅ Coverage analysis

### Phase 2: Advanced Algorithms (Week 2)
- ✅ Cycle detection
- ✅ Shortest path
- ✅ Critical path

### Phase 3: Optimization (Week 3)
- ✅ Query caching
- ✅ Batch operations
- ✅ Index optimization

### Phase 4: Performance Tuning (Week 4)
- ✅ Load testing
- ✅ Bottleneck analysis
- ✅ Optimization

---

## 🎯 PERFORMANCE TARGETS

- Impact analysis: <100ms
- Coverage analysis: <50ms
- Cycle detection: <50ms
- Shortest path: <100ms
- Critical path: <200ms
- Traceability matrix: <500ms

---

## 🔗 NEXT STEPS

1. Implement core algorithms
2. Add query caching
3. Create materialized views
4. Optimize indexes
5. Load test
6. Performance tune
7. Add monitoring
8. Document algorithms

