SACL MCP Server

# SACL: Semantic-Augmented Reranking and Localization - Implementation Guide ## Paper Information - **Title**: SACL: Understanding and Combating Textual Bias in Code Retrieval with Semantic-Augmented Reranking and Localization - **Authors**: Dhruv Gupta, Gayathri Ganesh Lakshmy, Yiqing Xie - **arXiv ID**: 2506.20081v2 - **Categories**: cs.CL, cs.AI - **DOI**: https://doi.org/10.48550/arXiv.2506.20081 - **License**: Creative Commons BY-SA 4.0 ## Overview SACL addresses a critical problem in code retrieval systems: **textual bias**. Current systems over-rely on surface-level features like docstrings and identifier names, leading to biased results that favor well-documented code regardless of functional relevance. ## Key Technical Components ### 1. Problem Identification - **Textual Bias**: Over-reliance on documentation and naming conventions - **Surface-level Feature Dependency**: Systems prioritize textual similarity over semantic/functional similarity - **Documentation Bias**: Well-documented but functionally irrelevant code ranks higher than poorly documented but functionally relevant code ### 2. SACL Framework Architecture Based on the available information, SACL implements a two-stage approach: #### Stage 1: Semantic Augmentation - **Purpose**: Enrich code representations with semantic information - **Method**: Augment structural knowledge with semantic understanding - **Focus**: Move beyond surface-level textual features #### Stage 2: Reranking and Localization - **Reranking**: Improve initial retrieval results using semantic signals - **Localization**: Identify and focus on functionally relevant code segments ### 3. Implementation Strategy #### A. Code Preprocessing and Feature Extraction ```python # Pseudo-implementation structure class SACLCodeProcessor: def __init__(self): self.semantic_extractor = SemanticFeatureExtractor() self.structural_analyzer = StructuralAnalyzer() self.bias_detector = TextualBiasDetector() def process_code(self, code_snippet): # Extract multiple types of features textual_features = self.extract_textual_features(code_snippet) semantic_features = self.semantic_extractor.extract(code_snippet) structural_features = self.structural_analyzer.analyze(code_snippet) return self.combine_features(textual_features, semantic_features, structural_features) ``` #### B. Bias Detection and Mitigation ```python class TextualBiasDetector: def __init__(self): self.bias_indicators = [ 'docstring_dependency', 'identifier_name_bias', 'comment_over_reliance' ] def detect_bias(self, retrieval_results, query): # Systematic masking approach mentioned in paper masked_results = self.mask_textual_features(retrieval_results) bias_score = self.calculate_bias_score(retrieval_results, masked_results) return bias_score def mask_textual_features(self, code): # Preserve functionality while masking textual elements # This is a key technique mentioned in the methodology pass ``` #### C. Semantic Augmentation Module ```python class SemanticAugmenter: def __init__(self): self.code_encoder = CodeEncoder() # Likely uses transformer-based models self.semantic_embedder = SemanticEmbedder() def augment_representation(self, code_snippet): # Create enhanced representations base_embedding = self.code_encoder.encode(code_snippet) semantic_embedding = self.semantic_embedder.embed(code_snippet) # Combine representations to reduce textual bias augmented_representation = self.combine_embeddings( base_embedding, semantic_embedding ) return augmented_representation ``` #### D. Reranking System ```python class SACLReranker: def __init__(self): self.similarity_calculator = SemanticSimilarityCalculator() self.relevance_scorer = FunctionalRelevanceScorer() def rerank(self, initial_results, query, top_k=10): scored_results = [] for result in initial_results: # Calculate multiple similarity scores textual_sim = self.calculate_textual_similarity(result, query) semantic_sim = self.similarity_calculator.calculate(result, query) functional_sim = self.relevance_scorer.score(result, query) # Weighted combination to reduce textual bias final_score = self.combine_scores(textual_sim, semantic_sim, functional_sim) scored_results.append((result, final_score)) # Sort and return top results return sorted(scored_results, key=lambda x: x[1], reverse=True)[:top_k] ``` ### 4. Technical Implementation Details #### Key Algorithms (Inferred) 1. **Feature Masking Algorithm**: Systematically mask textual features while preserving code functionality 2. **Semantic Embedding Enhancement**: Augment traditional code embeddings with semantic information 3. **Bias-Aware Ranking**: Combine multiple similarity metrics to reduce textual bias 4. **Localization Algorithm**: Identify functionally relevant code segments #### Data Structures ```python class CodeRepresentation: def __init__(self): self.textual_features = {} # Docstrings, comments, identifiers self.structural_features = {} # AST, control flow, data flow self.semantic_features = {} # Functional semantics, behavior patterns self.bias_score = 0.0 # Detected textual bias level self.augmented_embedding = [] # Enhanced representation vector class RetrievalResult: def __init__(self): self.code_snippet = "" self.original_score = 0.0 # Initial retrieval score self.semantic_score = 0.0 # Semantic similarity score self.bias_adjusted_score = 0.0 # Final SACL score self.localization_regions = [] # Relevant code segments ``` ### 5. Evaluation Metrics and Datasets #### Datasets Used - **HumanEval**: Improved by 12.8% Recall@1 - **MBPP**: Improved by 9.4% Recall@1 - **SWE-Bench-Lite**: Improved by 7.0% Recall@1 #### Performance Metrics - **Recall@K**: Primary retrieval evaluation metric - **Pass@1**: Code generation quality metric (4.88% improvement on HumanEval) - **Bias Reduction Score**: Custom metric for measuring textual bias mitigation #### Evaluation Protocol ```python class SACLEvaluator: def __init__(self): self.datasets = ['HumanEval', 'MBPP', 'SWE-Bench-Lite'] self.metrics = ['Recall@1', 'Recall@5', 'Recall@10', 'Pass@1'] def evaluate(self, sacl_system, baseline_system): results = {} for dataset in self.datasets: for metric in self.metrics: sacl_score = self.calculate_metric(sacl_system, dataset, metric) baseline_score = self.calculate_metric(baseline_system, dataset, metric) improvement = sacl_score - baseline_score results[f"{dataset}_{metric}"] = { 'sacl': sacl_score, 'baseline': baseline_score, 'improvement': improvement } return results ``` ### 6. Implementation Considerations #### Technical Requirements - **Model Architecture**: Transformer-based code encoders (likely CodeBERT, GraphCodeBERT, or similar) - **Embedding Dimensions**: High-dimensional vector spaces for semantic representations - **Computational Resources**: GPU acceleration for embedding generation and similarity calculations #### Integration Points - **Retrieval Systems**: Can be integrated with existing code search engines - **IDE Plugins**: Suitable for code completion and search features - **Code Review Tools**: Enhance code similarity detection and review suggestions ### 7. Limitations and Future Work #### Current Limitations (Inferred) - Computational overhead from semantic augmentation - Dependency on quality of semantic feature extraction - Need for domain-specific fine-tuning #### Potential Extensions - Multi-language code retrieval support - Real-time bias detection and mitigation - Integration with code generation models - Advanced localization techniques for large codebases ## Implementation Roadmap ### Phase 1: Core Infrastructure 1. Implement basic code preprocessing pipeline 2. Develop textual bias detection mechanisms 3. Create semantic feature extraction module ### Phase 2: SACL Framework 1. Build semantic augmentation system 2. Implement reranking algorithm 3. Develop localization capabilities ### Phase 3: Evaluation and Optimization 1. Reproduce benchmark results 2. Optimize for computational efficiency 3. Extend to additional datasets and use cases ### Phase 4: Production Deployment 1. Create API interfaces 2. Develop integration tools 3. Build monitoring and evaluation systems ## References and Related Work Based on the paper's citation patterns, key related work includes: - Dense Passage Retrieval (DPR) techniques - Retrieval-Augmented Code Completion systems - CodeRAG benchmark frameworks - Recent advances in code understanding and generation ## Notes This implementation guide is based on the available information from the SACL paper. For complete implementation details, including specific algorithms, hyperparameters, and architectural choices, access to the full paper content would be necessary. The framework described here provides a solid foundation for implementing the SACL approach based on the disclosed methodology and results. For the most accurate implementation, consider: 1. Accessing the full paper PDF for detailed technical specifications 2. Reviewing the authors' code repository if available 3. Consulting the complete experimental setup and hyperparameter configurations 4. Examining the specific datasets and evaluation protocols used ## Contact and Attribution - **Paper**: "SACL: Understanding and Combating Textual Bias in Code Retrieval with Semantic-Augmented Reranking and Localization" - **Authors**: Dhruv Gupta, Gayathri Ganesh Lakshmy, Yiqing Xie - **arXiv**: https://arxiv.org/abs/2506.20081v2 - **License**: CC BY-SA 4.0