--- title: "Phase 3.1: AI-Assisted Merge Conflict Resolution" labels: ["enhancement", "phase-3", "ai-ml", "medium-priority"] assignees: [] milestone: "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**: ```python 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**: ```python 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 ```python # 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 ```cpp // 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)