# Requirements Traceability - Deep Dive: AI/ML Integration

## AI-Enhanced Requirements Traceability

### Use Cases for AI/ML

1. **Automated Link Discovery**: NLP to find relationships between requirements and code
2. **Requirement Classification**: Classify requirements by type, priority, domain
3. **Duplicate Detection**: Find similar or duplicate requirements
4. **Gap Analysis**: Identify missing requirements or test cases
5. **Impact Prediction**: Predict change impact using ML models
6. **Requirement Quality**: Assess requirement quality (ambiguity, completeness)
7. **Test Case Generation**: Generate test cases from requirements
8. **Traceability Recommendation**: Suggest missing links

## Natural Language Processing (NLP)

### Requirement Extraction from Documents

```python
from transformers import pipeline

class RequirementExtractor:
    def __init__(self):
        self.ner = pipeline("ner", model="bert-base-cased")
        self.classifier = pipeline("text-classification", 
                                   model="requirement-classifier")
    
    def extract_requirements(self, document: str) -> List[Requirement]:
        """Extract requirements from natural language document"""
        sentences = self.split_sentences(document)
        requirements = []
        
        for sentence in sentences:
            # Classify if sentence is a requirement
            if self.is_requirement(sentence):
                req_type = self.classify_requirement(sentence)
                entities = self.extract_entities(sentence)
                
                requirements.append(Requirement(
                    title=self.extract_title(sentence),
                    description=sentence,
                    type=req_type,
                    entities=entities
                ))
        
        return requirements
    
    def is_requirement(self, sentence: str) -> bool:
        """Detect if sentence is a requirement"""
        # Look for requirement keywords
        keywords = ["shall", "must", "should", "will", "may"]
        return any(keyword in sentence.lower() for keyword in keywords)
    
    def classify_requirement(self, sentence: str) -> str:
        """Classify requirement type"""
        result = self.classifier(sentence)
        return result[0]['label']  # Functional, Non-functional, etc.
```

### Semantic Similarity for Link Discovery

```python
from sentence_transformers import SentenceTransformer
import numpy as np

class LinkDiscovery:
    def __init__(self):
        self.model = SentenceTransformer('all-MiniLM-L6-v2')
    
    def find_related_requirements(self, req: Requirement, 
                                  candidates: List[Requirement],
                                  threshold: float = 0.7) -> List[Tuple[Requirement, float]]:
        """Find semantically similar requirements"""
        req_embedding = self.model.encode(req.description)
        candidate_embeddings = self.model.encode([c.description for c in candidates])
        
        # Compute cosine similarity
        similarities = np.dot(candidate_embeddings, req_embedding) / (
            np.linalg.norm(candidate_embeddings, axis=1) * np.linalg.norm(req_embedding)
        )
        
        # Filter by threshold
        related = [
            (candidates[i], similarities[i])
            for i in range(len(candidates))
            if similarities[i] >= threshold
        ]
        
        return sorted(related, key=lambda x: x[1], reverse=True)
    
    def suggest_links(self, req: Requirement, code_files: List[CodeFile]) -> List[Link]:
        """Suggest links between requirement and code"""
        req_keywords = self.extract_keywords(req.description)
        suggestions = []
        
        for code_file in code_files:
            # Extract code comments and docstrings
            code_text = self.extract_text_from_code(code_file)
            
            # Compute similarity
            similarity = self.compute_similarity(req.description, code_text)
            
            if similarity >= 0.6:
                suggestions.append(Link(
                    source_id=req.id,
                    target_id=code_file.id,
                    link_type="implements",
                    confidence=similarity
                ))
        
        return suggestions
```

### Requirement Quality Assessment

```python
class RequirementQualityAnalyzer:
    def __init__(self):
        self.ambiguity_detector = AmbiguityDetector()
        self.completeness_checker = CompletenessChecker()
    
    def assess_quality(self, req: Requirement) -> QualityReport:
        """Assess requirement quality"""
        issues = []
        
        # Check for ambiguous words
        ambiguous_words = self.ambiguity_detector.detect(req.description)
        if ambiguous_words:
            issues.append(f"Ambiguous words: {', '.join(ambiguous_words)}")
        
        # Check completeness
        if not self.completeness_checker.has_acceptance_criteria(req):
            issues.append("Missing acceptance criteria")
        
        # Check testability
        if not self.is_testable(req):
            issues.append("Requirement is not testable")
        
        # Check atomicity
        if self.is_compound(req):
            issues.append("Requirement is compound (should be split)")
        
        return QualityReport(
            requirement_id=req.id,
            score=self.compute_score(issues),
            issues=issues
        )
    
    def is_testable(self, req: Requirement) -> bool:
        """Check if requirement is testable"""
        # Look for measurable criteria
        measurable_keywords = ["within", "at least", "no more than", "exactly"]
        return any(keyword in req.description.lower() for keyword in measurable_keywords)
```

## Machine Learning for Traceability

### Change Impact Prediction

```python
from sklearn.ensemble import RandomForestClassifier
import pandas as pd

class ImpactPredictor:
    def __init__(self):
        self.model = RandomForestClassifier(n_estimators=100)
        self.trained = False
    
    def train(self, historical_changes: List[Change]):
        """Train model on historical change data"""
        features = []
        labels = []
        
        for change in historical_changes:
            # Extract features
            feature_vector = self.extract_features(change)
            features.append(feature_vector)
            
            # Label: number of affected requirements
            labels.append(len(change.affected_requirements))
        
        X = pd.DataFrame(features)
        y = pd.Series(labels)
        
        self.model.fit(X, y)
        self.trained = True
    
    def predict_impact(self, change: Change) -> List[Requirement]:
        """Predict which requirements will be affected"""
        if not self.trained:
            raise ValueError("Model not trained")
        
        feature_vector = self.extract_features(change)
        predicted_count = self.model.predict([feature_vector])[0]
        
        # Find most likely affected requirements
        candidates = self.get_candidate_requirements(change)
        probabilities = self.model.predict_proba([feature_vector])[0]
        
        # Rank candidates by probability
        ranked = sorted(
            zip(candidates, probabilities),
            key=lambda x: x[1],
            reverse=True
        )
        
        return [req for req, prob in ranked[:predicted_count]]
    
    def extract_features(self, change: Change) -> dict:
        """Extract features for ML model"""
        return {
            'change_type': change.type,
            'num_files_changed': len(change.files),
            'lines_added': change.lines_added,
            'lines_deleted': change.lines_deleted,
            'num_existing_links': len(change.existing_links),
            'requirement_type': change.requirement.type,
            'priority': change.requirement.priority
        }
```

### Automated Test Case Generation

```python
class TestCaseGenerator:
    def __init__(self):
        self.llm = LLMClient()  # GPT-4, Claude, etc.
    
    def generate_test_cases(self, req: Requirement) -> List[TestCase]:
        """Generate test cases from requirement using LLM"""
        prompt = f"""
        Given the following requirement:
        
        Title: {req.title}
        Description: {req.description}
        Type: {req.type}
        
        Generate comprehensive test cases including:
        1. Positive test cases (happy path)
        2. Negative test cases (error conditions)
        3. Edge cases
        4. Boundary conditions
        
        Format each test case as:
        - Test Case ID
        - Description
        - Preconditions
        - Steps
        - Expected Result
        """
        
        response = self.llm.generate(prompt)
        test_cases = self.parse_test_cases(response)
        
        return test_cases
```

## AI Model Traceability

For AI/ML systems, requirements traceability extends to model artifacts:

### Model Traceability Graph
```
Business Requirement
    ↓ (decomposes_to)
Model Requirement
    ↓ (specifies)
Model Architecture
    ↓ (implements)
Training Code
    ↓ (produces)
Trained Model
    ↓ (validates)
Validation Dataset
    ↓ (tests)
Model Tests
    ↓ (monitors)
Production Metrics
```

### Model Requirement Types
- **Performance Requirements**: Accuracy, precision, recall, F1 score
- **Fairness Requirements**: Bias metrics, demographic parity
- **Robustness Requirements**: Adversarial robustness, out-of-distribution
- **Explainability Requirements**: Feature importance, SHAP values
- **Operational Requirements**: Latency, throughput, resource usage

### Model Validation Traceability

```python
class ModelTraceability:
    def __init__(self):
        self.storage = TraceabilityStorage()
    
    def link_model_to_requirements(self, model: MLModel, requirements: List[Requirement]):
        """Link trained model to requirements"""
        for req in requirements:
            # Create link
            link = Link(
                source_id=req.id,
                target_id=model.id,
                link_type="validates",
                metadata={
                    "validation_date": datetime.now(),
                    "metrics": model.metrics,
                    "dataset": model.validation_dataset
                }
            )
            self.storage.create_link(link)
    
    def verify_model_compliance(self, model: MLModel) -> ComplianceReport:
        """Verify model meets all requirements"""
        requirements = self.storage.get_linked_requirements(model.id)
        results = []
        
        for req in requirements:
            if req.type == "performance":
                passed = self.check_performance(model, req)
            elif req.type == "fairness":
                passed = self.check_fairness(model, req)
            elif req.type == "robustness":
                passed = self.check_robustness(model, req)
            
            results.append((req, passed))
        
        return ComplianceReport(model=model, results=results)
```

## MCP Integration for AI Agents

```python
# MCP tools for AI agent interaction
@mcp_tool
def create_requirement_from_conversation(conversation: str) -> Requirement:
    """AI agent creates requirement from natural language"""
    extractor = RequirementExtractor()
    requirements = extractor.extract_requirements(conversation)
    return requirements[0] if requirements else None

@mcp_tool
def suggest_missing_links(requirement_id: str) -> List[Link]:
    """AI agent suggests missing traceability links"""
    discovery = LinkDiscovery()
    req = storage.get_requirement(requirement_id)
    candidates = storage.get_all_requirements()
    return discovery.suggest_links(req, candidates)

@mcp_tool
def generate_test_cases_for_requirement(requirement_id: str) -> List[TestCase]:
    """AI agent generates test cases"""
    generator = TestCaseGenerator()
    req = storage.get_requirement(requirement_id)
    return generator.generate_test_cases(req)
```

