PROJECT_STRUCTURE.md

Linux Kernel Anti-Pattern Detector - Project Structure

Overview

This project is organized into a clear, maintainable structure that separates concerns and makes it easy to find, modify, and extend functionality.

Directory Structure

Linux Kernel Anti-Pattern Detector/
├── 📁 data/                          # Analysis data and results
│   ├── results.json                  # Main analysis results
│   ├── concurrency_analysis_report.json
│   └── kernel_analysis.log          # Analysis logs
│
├── 📁 docs/                          # Documentation
│   ├── kernel-analysis-guide.md     # Kernel analysis documentation
│   └── [additional documentation]
│
├── 📁 examples/                      # Example code and usage
│   └── [example files]
│
├── 📁 reports/                       # Generated analysis reports
│   ├── Linux_Kernel_Anti_Pattern_Analysis_Report.md
│   ├── Executive_Summary.md
│   └── 📁 concurrency/              # Concurrency-specific reports
│       └── Concurrency_Analysis_Report.md
│
├── 📁 scripts/                       # Analysis and utility scripts
│   ├── 📁 analysis/                 # Core analysis scripts
│   │   ├── concurrency_analyzer.py  # Concurrency issue analyzer
│   │   └── analyze_kernel_structure.py
│   ├── 📁 reporting/                # Report generation scripts
│   │   └── view_results.py         # Results viewer
│   └── 📁 utils/                    # Utility scripts
│       └── quick_summary.py        # Quick summary generator
│
├── 📁 src/                          # Source code (main project)
│   ├── __init__.py
│   ├── 📁 detectors/               # Anti-pattern detection modules
│   ├── 📁 rules/                   # Detection rules and patterns
│   └── 📁 utils/                   # Utility functions
│
├── 📁 tests/                        # Test files
│   └── [test files]
│
├── 📁 tools/                        # Analysis tools and detectors
│   ├── 📁 detectors/               # Main detection tools
│   │   ├── detector.py             # Main anti-pattern detector
│   │   └── config.yaml             # Detection configuration
│   ├── 📁 visualizers/             # Data visualization tools
│   └── 📁 exporters/               # Data export tools
│
├── 📁 linux/                        # Linux kernel source (cloned)
│   └── [kernel source files]
│
├── 📄 README.md                     # Main project documentation
├── 📄 requirements.txt              # Main project dependencies
├── 📄 requirements-kernel-analysis.txt
├── 📄 requirements-simple.txt
├── 📄 .gitignore                    # Git ignore rules
└── 📄 PROJECT_STRUCTURE.md          # This file

Directory Descriptions

📁 data/

Contains all analysis results, logs, and generated data files.

• results.json: Complete analysis results from the main detector
• concurrency_analysis_report.json: Detailed concurrency analysis
• kernel_analysis.log: Analysis execution logs

📁 docs/

Project documentation and guides.

• kernel-analysis-guide.md: Comprehensive guide for kernel analysis
• Additional documentation for specific features

📁 examples/

Example code, usage patterns, and sample data.

• Example kernel modules for testing
• Sample configuration files
• Usage examples

📁 reports/

Generated analysis reports in various formats.

• Linux_Kernel_Anti_Pattern_Analysis_Report.md: Complete technical report
• Executive_Summary.md: High-level summary for stakeholders
• concurrency/: Specialized reports for specific issue types

📁 scripts/

Analysis and utility scripts organized by function.

📁 analysis/

Core analysis scripts for different types of anti-patterns.

• concurrency_analyzer.py: Specialized concurrency issue analysis
• analyze_kernel_structure.py: Kernel structure analysis

📁 reporting/

Scripts for generating and viewing reports.

• view_results.py: Interactive results viewer and reporter

📁 utils/

Utility scripts for common tasks.

• quick_summary.py: Quick summary generation

📁 src/

Main project source code (core framework).

• detectors/: Anti-pattern detection modules
• rules/: Detection rules and pattern definitions
• utils/: Utility functions and helpers

📁 tests/

Test files and test data.

• Unit tests for detection modules
• Integration tests
• Test data and fixtures

📁 tools/

Analysis tools and detectors.

📁 detectors/

Main detection tools and configurations.

• detector.py: Primary anti-pattern detection engine
• config.yaml: Detection configuration and rules

📁 visualizers/

Data visualization and charting tools.

• Interactive dashboards
• Chart generators
• Data plotting utilities

📁 exporters/

Data export and format conversion tools.

• JSON to other formats
• Report generation
• Data transformation

📁 linux/

Cloned Linux kernel source code for analysis.

• Complete kernel source tree
• Used for code snippet extraction
• Reference for pattern validation

File Descriptions

Core Files

• README.md: Main project documentation and getting started guide
• requirements.txt: Main project Python dependencies
• requirements-kernel-analysis.txt: Kernel analysis specific dependencies
• requirements-simple.txt: Simplified dependencies for basic usage
• .gitignore: Git ignore patterns for the project

Configuration Files

• tools/detectors/config.yaml: Main detection configuration
• tools/detectors/detector.py: Primary detection engine

Usage Patterns

Running Analysis

# Main analysis
python tools/detectors/detector.py --clone --output data/results.json

# Concurrency analysis
python scripts/analysis/concurrency_analyzer.py

# View results
python scripts/reporting/view_results.py data/results.json

Generating Reports

# Quick summary
python scripts/utils/quick_summary.py

# Interactive viewer
python scripts/reporting/view_results.py --interactive

Development

# Install dependencies
pip install -r requirements.txt
pip install -r requirements-kernel-analysis.txt

# Run tests
python -m pytest tests/

# Development setup
conda activate linux-kernel-anti-pattern-detector

Best Practices

Adding New Features

1. Analysis scripts: Add to scripts/analysis/
2. Reporting tools: Add to scripts/reporting/
3. Utilities: Add to scripts/utils/
4. Core detection: Add to src/detectors/
5. Configuration: Update tools/detectors/config.yaml

File Naming Conventions

• Python files: snake_case (e.g., concurrency_analyzer.py)
• Configuration files: kebab-case (e.g., kernel-analysis-guide.md)
• Reports: Pascal_Case (e.g., Concurrency_Analysis_Report.md)

Data Management

• Raw data: Store in data/
• Processed results: Store in data/
• Reports: Generate in reports/
• Logs: Store in data/

Maintenance

Regular Tasks

1. Update dependencies: Review and update requirements files
2. Clean data: Remove old analysis results periodically
3. Update kernel: Refresh the Linux kernel source
4. Backup reports: Archive important analysis reports

Version Control

• Track: Source code, configuration, documentation
• Ignore: Analysis results, logs, kernel source (large files)
• Archive: Important reports and findings

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This structure is designed to be scalable, maintainable, and easy to navigate. Each directory has a clear purpose and the organization supports both development and research workflows.