WizardMerge/.github/issues/08-ai-assisted-merging.md

title, labels, assignees, milestone

title	labels	assignees	milestone
Phase 3.1: AI-Assisted Merge Conflict Resolution	enhancement phase-3 ai-ml medium-priority		Phase 3 - Advanced Features

Overview

Integrate AI/ML capabilities to provide intelligent merge conflict resolution suggestions, pattern recognition from repository history, and natural language explanations of conflicts.

Related Roadmap Section

Phase 3.1 - AI-Assisted Merging

Motivation

While SDG analysis provides structural insights, AI can:

• Learn from historical resolutions in the codebase
• Recognize patterns across projects
• Provide natural language explanations
• Suggest context-aware resolutions
• Assess risk of resolution choices

Features to Implement

1. ML Model for Conflict Resolution

Train a machine learning model to suggest resolutions based on:

• Code structure (AST features)
• Historical resolutions in the repo
• Common patterns in similar codebases
• Developer intent (commit messages, PR descriptions)

Model Types to Explore:

• Decision Tree / Random Forest: For rule-based classification
• Neural Network: For complex pattern recognition
• Transformer-based: For code understanding (CodeBERT, GraphCodeBERT)
• Hybrid: Combine SDG + ML for best results

Features for ML Model:

features = {
    # Structural features
    'conflict_size': int,              # Lines in conflict
    'conflict_type': str,              # add/add, modify/modify, etc.
    'file_type': str,                  # .py, .js, .java
    'num_dependencies': int,           # From SDG
    
    # Historical features
    'similar_resolutions': List[str],  # Past resolutions in repo
    'author_ours': str,                # Who made 'ours' change
    'author_theirs': str,              # Who made 'theirs' change
    
    # Semantic features
    'ast_node_type': str,              # function, class, import, etc.
    'variable_names': List[str],       # Variables involved
    'function_calls': List[str],       # Functions called
    
    # Context features
    'commit_message_ours': str,        # Commit message for 'ours'
    'commit_message_theirs': str,      # Commit message for 'theirs'
    'pr_description': str,             # PR description (if available)
}

2. Pattern Recognition from Repository History

Analyze past conflict resolutions in the repository:

• Mining Git history:

  • Find merge commits
  • Extract conflicts and their resolutions
  • Build training dataset

• Pattern extraction:

  • Common resolution strategies (keep ours, keep theirs, merge both)
  • File-specific patterns (package.json always merges dependencies)
  • Developer-specific patterns (Alice tends to keep UI changes)

• Pattern matching:

  • Compare current conflict to historical patterns
  • Find most similar past conflicts
  • Suggest resolutions based on similarity

Algorithm:

def find_similar_conflicts(current_conflict, history):
    # 1. Extract features from current conflict
    features = extract_features(current_conflict)
    
    # 2. Compute similarity to historical conflicts
    similarities = []
    for past_conflict in history:
        sim = cosine_similarity(features, past_conflict.features)
        similarities.append((sim, past_conflict))
    
    # 3. Return top-k most similar
    return sorted(similarities, reverse=True)[:5]

def suggest_resolution(current_conflict, similar_conflicts):
    # Majority vote from similar conflicts
    resolutions = [c.resolution for c in similar_conflicts]
    return most_common(resolutions)

3. Natural Language Explanations

Generate human-readable explanations of conflicts and suggestions:

Example:

Conflict in file: src/utils.py
Location: function calculate()

Explanation:
- BASE: The function returned x * 2
- OURS: Changed return value to x * 3 (commit abc123 by Alice: "Increase multiplier")
- THEIRS: Changed return value to x + 1 (commit def456 by Bob: "Use addition instead")

Dependencies affected:
- 3 functions call calculate() in this file
- 2 test cases depend on the return value

Suggestion: Keep OURS (confidence: 75%)
Reasoning:
- Alice's change (x * 3) maintains the multiplication pattern used elsewhere
- Bob's change (x + 1) alters the semantic meaning significantly
- Historical resolutions in similar functions favor keeping the multiplication

Risk: MEDIUM
- Test case test_calculate() may need updating
- Consider reviewing with Bob to understand intent

Implementation:

• Template-based generation for simple cases
• GPT/LLM-based generation for complex explanations
• Integrate commit messages and PR context
• Explain SDG dependencies in plain language

4. Context-Aware Code Completion

During conflict resolution, provide intelligent code completion:

• Integrate with LSP (Language Server Protocol)
• Suggest imports needed for resolution
• Validate syntax in real-time
• Auto-complete variables/functions from context
• Suggest type annotations (TypeScript, Python)

5. Risk Assessment for Resolution Choices

Assess the risk of each resolution option:

┌──────────────────────────────────────┐
│ Resolution Options                   │
├──────────────────────────────────────┤
│ ✓ Keep OURS         Risk: LOW   ●○○ │
│   - Maintains existing tests         │
│   - Consistent with codebase style   │
│                                      │
│ ○ Keep THEIRS       Risk: HIGH  ●●● │
│   - Breaks 3 test cases              │
│   - Incompatible with feature X      │
│                                      │
│ ○ Merge both        Risk: MED   ●●○ │
│   - Requires manual adjustment       │
│   - May cause runtime error          │
└──────────────────────────────────────┘

Risk Factors:

• Test coverage affected
• Number of dependencies broken
• Semantic compatibility
• Historical success rate
• Developer confidence

Technical Design

ML Pipeline

# Training pipeline
class ConflictResolutionModel:
    def __init__(self):
        self.model = None  # Transformer or other model
        self.feature_extractor = FeatureExtractor()
    
    def train(self, training_data):
        """Train on historical conflicts and resolutions"""
        features = [self.feature_extractor.extract(c) for c in training_data]
        labels = [c.resolution for c in training_data]
        self.model.fit(features, labels)
    
    def predict(self, conflict):
        """Predict resolution for new conflict"""
        features = self.feature_extractor.extract(conflict)
        prediction = self.model.predict(features)
        confidence = self.model.predict_proba(features)
        return prediction, confidence

# Feature extraction
class FeatureExtractor:
    def extract(self, conflict):
        return {
            'structural': self.extract_structural(conflict),
            'historical': self.extract_historical(conflict),
            'semantic': self.extract_semantic(conflict),
            'contextual': self.extract_contextual(conflict),
        }

Integration with WizardMerge

// C++ backend integration
class AIAssistant {
public:
  // Get AI suggestion for conflict
  ResolutionSuggestion suggest(const Conflict& conflict);
  
  // Get natural language explanation
  std::string explain(const Conflict& conflict);
  
  // Assess risk of resolution
  RiskAssessment assess_risk(const Conflict& conflict, Resolution resolution);
  
private:
  // Call Python ML service
  std::string call_ml_service(const std::string& endpoint, const Json::Value& data);
};

ML Service Architecture

┌─────────────────────┐
│  WizardMerge C++    │
│  Backend            │
└──────────┬──────────┘
           │ HTTP/gRPC
           ▼
┌─────────────────────┐
│  ML Service         │
│  (Python/FastAPI)   │
├─────────────────────┤
│ - Feature Extraction│
│ - Model Inference   │
│ - NLP Generation    │
│ - Risk Assessment   │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│  Model Storage      │
│  - Trained models   │
│  - Feature cache    │
│  - Historical data  │
└─────────────────────┘

Implementation Steps

Phase 1: Data Collection & Preparation (2 weeks)

• Mine Git history for conflicts and resolutions
• Build training dataset
• Feature engineering
• Data cleaning and validation

Phase 2: Model Training (3 weeks)

• Implement feature extraction
• Train baseline models (Decision Tree, Random Forest)
• Evaluate performance
• Experiment with advanced models (Transformers)
• Hyperparameter tuning

Phase 3: ML Service (2 weeks)

• Create Python FastAPI service
• Implement prediction endpoints
• Model serving and caching
• Performance optimization

Phase 4: Integration (2 weeks)

• Integrate ML service with C++ backend
• Add AI suggestions to merge API
• Update UI to display suggestions
• Add confidence scores

Phase 5: Natural Language Generation (2 weeks)

• Implement explanation templates
• Integrate with LLM (OpenAI API or local model)
• Context extraction (commits, PRs)
• UI for displaying explanations

Phase 6: Risk Assessment (1 week)

• Implement risk scoring
• Test impact analysis
• Dependency impact analysis
• UI for risk display

Phase 7: Testing & Refinement (2 weeks)

• User testing
• Model performance evaluation
• A/B testing (with and without AI)
• Collect feedback and iterate

Technologies

• ML Framework: PyTorch or TensorFlow
• NLP: Hugging Face Transformers, OpenAI API
• Feature Extraction: tree-sitter (AST), Git2 (history)
• ML Service: FastAPI (Python)
• Model Serving: TorchServe or TensorFlow Serving
• Vector Database: Pinecone or FAISS (for similarity search)

Acceptance Criteria

• ML model trained on historical data
• Achieves >70% accuracy on test set
• Provides suggestions in <1 second
• Natural language explanations are clear
• Risk assessment is accurate (validated by users)
• Integrates seamlessly with existing UI
• Falls back gracefully when ML unavailable
• User satisfaction >85%

Test Cases

Model Accuracy

1. Train on 80% of conflicts, test on 20%
2. Evaluate precision, recall, F1 score
3. Compare to baseline (SDG-only)

User Studies

1. Conflict resolution time (with vs without AI)
2. User satisfaction survey
3. Accuracy of AI suggestions (user feedback)
4. Usefulness of explanations

Performance

1. Prediction latency <1s
2. Explanation generation <2s
3. Risk assessment <500ms

Priority

MEDIUM - Advanced feature for Phase 3, builds on SDG analysis

Estimated Effort

14 weeks (3-4 months)

Dependencies

• SDG analysis (Issue #TBD)
• AST-based merging (Issue #TBD)
• Git history mining

Related Issues

• #TBD (Phase 3 tracking)
• #TBD (SDG Analysis)
• #TBD (Natural language processing)

Success Metrics

• 30% reduction in conflict resolution time (beyond SDG)
• 80% accuracy for AI suggestions
• 90% user satisfaction with explanations
• <1s latency for all AI features

Ethical Considerations

• Ensure ML model doesn't learn sensitive code patterns
• Provide transparency in AI decisions
• Allow users to disable AI features
• Don't store sensitive repository data
• Comply with data privacy regulations

Future Enhancements

• Fine-tune on user's specific codebase
• Federated learning across multiple repos
• Reinforcement learning from user feedback
• Multi-modal learning (code + documentation + issues)