debtmap 0.2.3

# DebtMap Architecture

## Overview

DebtMap is a high-performance technical debt analyzer that provides unified analysis of code quality metrics across multiple programming languages. The architecture is designed for scalability, performance, and extensibility.

## Core Components

### 1. Language Analyzers
- **FileAnalyzer**: Trait-based abstraction for language-specific analysis
- **RustAnalyzer**: Rust-specific implementation using syn for AST parsing
- **PythonAnalyzer**: Python-specific implementation using tree-sitter
- **Support for**: Rust, Python, JavaScript, TypeScript, Go

### 2. Unified Analysis Engine
- **UnifiedAnalysis**: Coordinates all analysis phases
- **ParallelUnifiedAnalysis**: High-performance parallel implementation
- **DebtAggregator**: Aggregates metrics across functions and files

### 3. Metrics Collection
- **Cyclomatic Complexity**: Control flow complexity measurement
- **Cognitive Complexity**: Human readability assessment  
- **Function Metrics**: Lines of code, parameters, nesting depth
- **File Metrics**: Module-level aggregation
- **Test Coverage**: Integration with lcov data

## Parallel Processing Architecture

### Overview
The parallel processing system leverages Rayon for CPU-bound parallel execution, enabling analysis of large codebases in sub-second time for typical projects.

### Parallelization Strategy

#### Phase 1: Initialization (Parallel)
All initialization tasks run concurrently using Rayon's parallel iterators:
- **Data Flow Graph Construction**: Build control and data flow graphs
- **Purity Analysis**: Identify pure vs impure functions
- **Test Detection**: Optimized O(n) detection with caching
- **Initial Debt Aggregation**: Baseline metric collection

#### Phase 2: Analysis (Parallel with Batching)
- **Function Analysis**: Process functions in configurable batches
- **File Analysis**: Parallel file-level metric aggregation
- **Batch Size**: Default 100 items, tunable via options

#### Phase 3: Aggregation (Sequential)
- **Result Merging**: Combine parallel results
- **Sorting**: Priority-based ranking
- **Final Scoring**: Apply weights and thresholds

### Performance Optimizations

#### Test Detection Optimization
```rust
// Original O(n²) approach
for function in functions {
    for test in tests {
        // Check if function is called by test
    }
}

// Optimized O(n) approach with caching
let test_cache = build_test_cache(&tests);
functions.par_iter().map(|f| {
    test_cache.is_tested(f)  // O(1) lookup
})
```

#### Parallel Configuration
- **Default**: Uses all available CPU cores
- **Configurable**: `--jobs N` flag for explicit control
- **Adaptive**: Batch size adjusts based on workload

### Thread Safety

#### Shared State Management
- **Arc<RwLock>**: For read-heavy shared data (call graphs, metrics)
- **Arc<Mutex>**: For write-heavy operations (progress tracking)
- **Immutable Structures**: Prefer immutable data where possible

#### Lock-Free Operations
- Use atomic operations for counters
- Batch updates to reduce contention
- Local accumulation with final merge

### Performance Targets

| Codebase Size | Target Time | Actual (Parallel) | Actual (Sequential) |
|---------------|-------------|-------------------|---------------------|
| 50 files      | <0.5s       | ~0.3s            | ~1.2s              |
| 250 files     | <1s         | ~0.8s            | ~5s                |
| 1000 files    | <5s         | ~3.5s            | ~20s               |

### Memory Management

#### Streaming Architecture
- Process files in batches to control memory usage
- Release intermediate results after aggregation
- Use iterators over collections where possible

#### Cache Efficiency
- Test detection cache reduces redundant computation
- Function signature caching for call graph
- Metric result caching for unchanged files

## Data Flow

```
Input Files
    ↓
[Parallel] Parse AST
    ↓
[Parallel] Extract Metrics
    ↓
[Parallel] Build Call Graph
    ↓
[Parallel] Detect Tests
    ↓
[Parallel] Calculate Debt
    ↓
[Sequential] Aggregate Results
    ↓
[Sequential] Apply Weights
    ↓
Output Report
```

## Configuration

### Performance Tuning Options

#### Command Line Flags
- `--jobs N`: Number of parallel jobs (default: CPU count)
- `--batch-size N`: Items per batch (default: 100)
- `--no-parallel`: Disable parallel processing
- `--progress`: Show progress indicators

#### Environment Variables
- `RAYON_NUM_THREADS`: Override thread pool size
- `DEBTMAP_BATCH_SIZE`: Default batch size
- `DEBTMAP_CACHE_DIR`: Cache location for incremental analysis

### Adaptive Behavior
The system automatically adjusts based on:
- Available CPU cores
- System memory
- Codebase size
- File complexity distribution

## Extension Points

### Adding Language Support
1. Implement the `FileAnalyzer` trait
2. Add parser integration (tree-sitter, syn, etc.)
3. Map language constructs to unified metrics
4. Register analyzer in the factory

### Custom Metrics
1. Extend `FunctionMetrics` or `FileMetrics`
2. Add calculation in analyzer implementation
3. Update aggregation logic
4. Modify weight configuration

### Analysis Plugins
1. Implement analysis phase interface
2. Register in unified analysis pipeline
3. Ensure thread-safety for parallel execution
4. Add configuration options

## Testing Strategy

### Unit Tests
- Individual component testing
- Mock dependencies for isolation
- Property-based testing for algorithms

### Integration Tests
- End-to-end analysis validation
- Performance regression tests
- Parallel vs sequential consistency checks

### Benchmarks
- Micro-benchmarks for critical paths
- Macro-benchmarks on real codebases
- Performance comparison suite

## Future Enhancements

### Planned Optimizations
- Incremental analysis with file watching
- Distributed analysis across machines
- GPU acceleration for graph algorithms
- Advanced caching strategies

### Scalability Improvements
- Streaming parser for huge files
- Database backend for enterprise scale
- Cloud-native deployment options
- Real-time analysis integration

## Dependencies

### Core Dependencies
- **rayon**: Parallel execution framework
- **syn**: Rust AST parsing
- **tree-sitter**: Multi-language parsing
- **serde**: Serialization
- **clap**: CLI argument parsing

### Language-Specific
- **tree-sitter-python**: Python support
- **tree-sitter-javascript**: JS/TS support
- **tree-sitter-go**: Go support

## Error Handling

### Resilience Strategy
- Graceful degradation on parser errors
- Partial results on analysis failure
- Detailed error reporting with context
- Recovery mechanisms for parallel failures

### Monitoring
- Performance metrics collection
- Error rate tracking
- Resource usage monitoring
- Analysis quality metrics