Improve Duplicate Detection

[milliondreams]

Priority: P2 - Missing Analysis Features Implementation

Description:
Enhance the duplicate detection beyond the current Jaccard coefficient approach with sophisticated structural analysis and semantic understanding.

Requirements:
Advanced Similarity Detection:

• Abstract Syntax Tree (AST) based comparison
• Semantic similarity beyond textual matching
• Variable renaming and refactoring detection

Structural Pattern Analysis:

• Function structure similarity
• Control flow pattern matching
• Design pattern duplication detection

Intelligent Thresholds:

• Context-aware similarity thresholds
• Language-specific duplication patterns
• False positive reduction mechanisms

Refactoring Suggestions:

• Extract method/function suggestions
• Common interface identification
• Code consolidation recommendations

Acceptance Criteria:

• Significantly improved duplicate detection accuracy
• Reduced false positives compared to current implementation
• Provides actionable refactoring suggestions
• Handles complex structural duplications
• Performance improvement over current algorithm

Estimated Timeline: 1-2 weeks

[milliondreams]

Milestone 2 Implementation Plan - Issue #14: Improve Duplicate Detection

Current State Analysis

• Basic Jaccard coefficient-based duplicate detection exists in crates/codeprism-analysis/src/duplicates.rs
• Current implementation focuses on textual similarity
• Missing advanced structural and semantic analysis capabilities

Detailed Solution Approach

1. Enhanced Similarity Detection

pub enum SimilarityType {
    Textual(JaccardSimilarity),
    Structural(AstSimilarity),
    Semantic(SemanticSimilarity),
    Hybrid(Vec<SimilarityType>),
}

pub struct AdvancedDuplicateAnalyzer {
    similarity_engines: Vec<Box<dyn SimilarityEngine>>,
    threshold_config: SimilarityThresholds,
}

2. Abstract Syntax Tree (AST) Based Comparison

• Parse code into language-agnostic AST representation
• Compare structural patterns ignoring variable names
• Detect refactored code that maintains same logic
• Handle renamed functions and variables intelligently

3. Semantic Similarity Analysis

• Analyze control flow patterns and algorithmic structure
• Detect similar business logic with different implementations
• Consider variable usage patterns and data flow
• Identify functionally equivalent code blocks

4. Intelligent Thresholding System

#[derive(Debug, Clone)]
pub struct SimilarityThresholds {
    pub textual_threshold: f64,
    pub structural_threshold: f64,
    pub semantic_threshold: f64,
    pub language_specific_adjustments: HashMap<Language, f64>,
    pub context_aware_scaling: bool,
}

5. Refactoring Suggestions Engine

• Generate extract-method suggestions for common patterns
• Identify opportunities for shared interfaces
• Suggest design patterns to eliminate duplication
• Provide automated refactoring scripts where possible

6. Performance Optimizations

• Implement efficient clustering algorithms for large codebases
• Use locality-sensitive hashing for fast similarity detection
• Add incremental analysis for changed files only
• Parallel processing for independent similarity comparisons

Implementation Timeline

• Week 1: Enhanced similarity algorithms and AST comparison
• Week 2: Semantic analysis and intelligent thresholding
• Week 3: Refactoring suggestions and performance optimization

Testing Strategy

• Create comprehensive test suite with known duplicate patterns
• Validate against real-world codebases
• Measure precision/recall improvements over current implementation
• Performance benchmarks for large repositories

This enhanced duplicate detection will provide significantly more accurate results and actionable refactoring guidance.

[milliondreams]

Starting Implementation

Working on improving Duplicate Detection as part of Milestone 2.

Implementation Approach:

1. AST-Based Comparison: Replace simple Jaccard coefficient with sophisticated AST structural analysis
2. Semantic Understanding: Add semantic similarity detection beyond textual matching
3. Advanced Pattern Recognition: Implement function structure similarity and control flow pattern matching
4. Smart Filtering: Context-aware thresholds and false positive reduction

Technical Implementation:

• Enhance duplicates.rs with AST parsing and comparison
• Add semantic normalization for variable names and structure
• Implement tree-sitter based structural analysis
• Add intelligent refactoring suggestions

Starting implementation now...

[milliondreams]

✅ COMPLETED

Duplicate Detection has been significantly enhanced beyond the original requirements:

✅ Implementation Complete:

• AST-Based Analysis: Replaced simple Jaccard coefficient with sophisticated AST comparison
• Semantic Understanding: Pattern recognition for API, database, validation, and error handling patterns
• Multiple Detection Types: Exact copy, structural similarity, semantic similarity, and pattern-based
• Intelligence Refactoring: Automated suggestions with effort estimation and implementation steps
• Language Support: Comprehensive analyzers for Python, JavaScript, and TypeScript

🎯 Key Features:

• Advanced Similarity: Structural hashing, normalized comparison, confidence scoring
• Smart Suggestions: Extract functions, create base classes, strategy patterns, template methods
• Complexity Analysis: Integrated complexity scoring with optimization recommendations
• Production Ready: Comprehensive test coverage and real-world applicable analysis

Issue closed in commit f3d9b92 🎉

[milliondreams]

✅ MILESTONE 2 COMPLETED

Enhanced Duplicate Detection fully implemented with advanced AST-based analysis and intelligent refactoring suggestions. See detailed implementation summary above.