Enhance LSP Diagnostics with Advanced Context Extraction and Error Correlation

[codegen-sh]

🎯 Overview

This PR significantly enhances the LSP diagnostics system with comprehensive context extraction and advanced error correlation capabilities, providing much richer diagnostic information for effective runtime and UI error analysis.

🔧 Key Enhancements

1. Context Extraction System

• CallerContextExtractor: Extracts detailed caller information from execution stack
• ModuleContextManager: Analyzes module structure using Python AST
• Rich Context: Provides comprehensive context for better error understanding

2. Advanced Error Correlation Analysis

• Pattern Detection: Identifies error patterns and frequencies
• Cross-Module Correlation: Analyzes errors across different modules
• Severity Alignment: Correlates diagnostic severity with runtime errors
• Scoring System: Provides correlation scores (0.0-1.0) for error relationships

3. Enhanced Diagnostic Structure

• New Context Fields: Added caller_context, module_context, and error_correlation
• Integrated Analysis: Combines LSP, runtime, and UI error information
• Comprehensive Context: Provides full context for each diagnostic

🧪 Testing & Validation

✅ All validation tests passed (4/4)

• CallerContextExtractor functionality ✅
• ModuleContextManager functionality ✅
• Error Correlation Analysis ✅
• Enhanced Diagnostic Structure ✅

📊 Technical Implementation

New Components Added

• CallerContextExtractor class for stack trace analysis
• ModuleContextManager class for AST-based module analysis
• _analyze_error_correlation() method for comprehensive error analysis
• _calculate_correlation_score() method for quantifying error relationships

Enhanced Data Structure

enhanced_diagnostic = {
    "diagnostic": {...},
    "caller_context": {
        "caller_frame": {...},
        "code_context": {...}
    },
    "module_context": {
        "file_path": "...",
        "definitions": {...},
        "imports": [...]
    },
    "error_correlation": {
        "error_patterns": {...},
        "cross_module_errors": [...],
        "frequency_analysis": {...},
        "severity_correlation": {...}
    }
}

🚀 Benefits

1. Better Error Context

• Rich context information for each diagnostic
• Caller analysis to understand error origins
• Module structure and relationship analysis

2. Error Correlation Detection

• Pattern recognition for recurring errors
• Cross-module error relationship tracking
• Frequency analysis and severity correlation

3. Enhanced Debugging Capabilities

• Comprehensive diagnostics combining multiple error sources
• Context-aware analysis with relevant information
• Quantified relationships between different error types

🔄 Integration

• Seamless Integration: Works with existing autogenlib context system
• Backward Compatible: Maintains compatibility with current diagnostic flow
• Performance Optimized: Lazy loading and caching for efficient operation

📋 Files Modified

• src/codegen/sdk/extensions/lsp/lsp_diagnostics.py - Core enhancements
• tests/test_enhanced_lsp_diagnostics.py - Comprehensive test suite
• ENHANCED_LSP_DIAGNOSTICS_SUMMARY.md - Detailed documentation

🎯 Impact

The enhanced LSP diagnostics system now provides:

• Comprehensive Context Extraction
• Advanced Error Correlation Analysis
• Rich Diagnostic Information
• Pattern Recognition Capabilities
• Cross-Module Error Tracking
• Quantified Error Relationships

This significantly improves the developer experience by providing much richer context for understanding and resolving issues in the codebase.

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💻 View my work • 👤 Initiated by @Zeeeepa • About Codegen
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Description by Korbit AI

What change is being made?

Enhance the LSP (Language Server Protocol) Diagnostics by incorporating advanced context extraction and error correlation features, including the addition of comprehensive context fields, an updated diagnostics analysis strategy, and extensive testing.

Why are these changes being made?

This enhancement aims to significantly improve the diagnostic capabilities of our LSP system by integrating detailed context extraction and error correlation analysis, enabling better error identification and mitigation strategies. These improvements facilitate a more effective debugging process, increasing developer efficiency and code reliability.

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