Linux Kernel Anti-Pattern Detector

A comprehensive tool for detecting anti-patterns and potential issues in Linux kernel code.

🎯 Overview

This project provides automated static analysis tools to identify common anti-patterns, code smells, and potential issues in Linux kernel source code. The analysis covers 7 major categories including memory management, concurrency, security vulnerabilities, and code quality issues.

📊 Recent Analysis Results

Analysis Date: June 29, 2025
Kernel Version: Linux 6.16-rc4
Files Analyzed: 35,588
Total Issues Found: 3,122

Issue Distribution

🔴 Critical Security Issues: 347 (11.1%)
🟡 High Priority Issues: 2,670 (85.5%)
🔵 Medium Priority Issues: 105 (3.4%)

Top Categories

Concurrency Issues: 2,314 (74.1%) - Race conditions, deadlocks
Memory Management: 356 (11.4%) - Memory leaks, use-after-free
Security Vulnerabilities: 347 (11.1%) - Buffer overflows, format strings
Code Quality: 92 (2.9%) - Magic numbers, code duplication

🚀 Quick Start

Prerequisites

Python 3.8+
Git
Conda (recommended for environment management)

Setup

# Clone the repository
git clone https://github.com/Mac-Huang/linux-kernel-anti-pattern-detector.git
cd linux-kernel-anti-pattern-detector

# Create and activate conda environment
conda create -n linux-kernel-anti-pattern-detector python=3.10 -y
conda activate linux-kernel-anti-pattern-detector

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

Run Analysis

# Full kernel analysis (clones kernel if needed)
python tools/detectors/detector.py --clone --output data/results.json

# Concurrency-specific analysis
python scripts/analysis/concurrency_analyzer.py

# View results interactively
python scripts/reporting/view_results.py --interactive

🗃️ Dataset Building Pipeline

✅ Successfully Implemented

The project includes a robust dataset building pipeline for training code intelligence models on Linux kernel bug fixes.

Dataset Format

{
  "input": {
    "original code": "code before the patch (extracted from a known bug-fix)",
    "instruction": "the commit message describing what this fix is"
  },
  "output": {
    "diff codes": "the unified diff patch for the bug fix"
  }
}

Features

🔍 Intelligent Bug Detection: Uses keyword-based filtering to identify bug-fix commits
📝 Focused Code Extraction: Extracts relevant code context around bug fixes
🔧 Diff Processing: Parses and formats unified diff patches
⚡ Parallel Processing: Multi-threaded processing for large repositories
📊 Quality Filtering: Only includes valid C source file modifications

Usage

# Activate environment
conda activate detector

# Build test dataset (small sample)
cd dataset_builder
python build_dataset_demo.py

# Build full dataset (entire repository)
# Edit TEST_MODE = False in build_dataset_demo.py
python build_dataset_demo.py

Output

File: dataset_builder/output/linux_bugfix_dataset.jsonl
Format: JSONL (one JSON object per line)
Content: Bug-fix commits with original code, commit messages, and diff patches

Keywords Detected

Memory issues: leak, null, overflow, memory
Security: security, vulnerability, exploit, buffer
Concurrency: race, deadlock, lock
General bugs: fix, bug, error, failure, crash

📁 Project Structure

├── 📁 data/                    # Analysis results and logs
├── 📁 dataset_builder/         # Dataset building pipeline
│   ├── build_dataset.py        # Main dataset builder
│   ├── build_dataset_demo.py   # Test dataset builder
│   └── output/                 # Generated datasets
├── 📁 docs/                    # Documentation
├── 📁 reports/                 # Generated reports
├── 📁 scripts/                 # Analysis and utility scripts
├── 📁 src/                     # Core source code
├── 📁 tests/                   # Test files
├── 📁 tools/                   # Detection tools
└── 📁 linux/                   # Kernel source (cloned)

See PROJECT_STRUCTURE.md for detailed structure information.

📋 Available Reports

📄 Main Reports

Complete Analysis Report - Full technical analysis
Executive Summary - High-level overview for stakeholders

🔒 Specialized Reports

Concurrency Analysis - Detailed concurrency issues (2,314 issues)

🛠️ Usage Examples

Basic Analysis

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

# Quick summary
python scripts/utils/quick_summary.py

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

Specialized Analysis

# Concurrency issues only
python scripts/analysis/concurrency_analyzer.py

# Kernel structure analysis
python scripts/analysis/analyze_kernel_structure.py

Custom Configuration

# Use custom config
python tools/detectors/detector.py --config tools/detectors/config.yaml

# Analyze specific kernel path
python tools/detectors/detector.py --kernel-path /path/to/kernel

🔍 Detection Categories

1. Memory Management

Memory leaks (kmalloc without kfree)
Use-after-free bugs
Double-free issues
Null pointer dereferences

2. Concurrency

Race conditions
Deadlocks
Missing locks
Double locking
Lock ordering violations

3. Security

Buffer overflows
Format string vulnerabilities
Privilege escalation
Information disclosure

4. Error Handling

Unchecked return values
Missing error handling
Ignored error codes
Wrong error propagation

5. Performance

O(n²) algorithms
Unnecessary memory allocation
Inefficient data structures
Cache miss patterns

6. Code Quality

Magic numbers
Hardcoded values
Complex functions
Code duplication

7. API Usage

Deprecated functions
Wrong API usage
Missing parameter validation
Incorrect flags

📊 Analysis Features

Pattern-based Detection: Regular expression matching with context awareness
Parallel Processing: Configurable concurrent analysis for performance
Detailed Reporting: JSON output with file locations and line numbers
Interactive Viewer: Browse and filter results by category and severity
Code Snippet Extraction: View actual code around detected issues
Severity Classification: Critical, High, Medium, Low priority levels

🔧 Configuration

The analysis can be customized through tools/detectors/config.yaml:

detection_rules:
  memory_management:
    enabled: true
    severity: "high"
    patterns:
      - "kmalloc.*without.*kfree"
      - "use.*after.*free"
    directories: ["drivers", "kernel", "mm"]

📈 Performance

Analysis Speed: ~11 minutes for 35,588 files
Memory Usage: Configurable limits (default: 1GB)
Parallel Processing: Up to 4 concurrent analyzers
File Filtering: Excludes generated files and build artifacts

🤝 Contributing

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Add your changes following the project structure
Test your changes (python -m pytest tests/)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

Development Guidelines

Analysis scripts: Add to scripts/analysis/
Reporting tools: Add to scripts/reporting/
Core detection: Add to src/detectors/
Configuration: Update tools/detectors/config.yaml

📚 Documentation

Project Structure - Detailed project organization
Kernel Analysis Guide - Comprehensive analysis guide
Concurrency Analysis - Concurrency-specific findings

🐛 Known Issues

Code snippet extraction: Some file paths may not match due to kernel cloning method
Large file handling: Files >10MB are skipped to prevent memory issues
Pattern accuracy: Some patterns may generate false positives

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

Linux kernel community for the source code
Static analysis research community
Contributors and maintainers

📞 Contact

Repository: https://github.com/Mac-Huang/linux-kernel-anti-pattern-detector
Issues: Use GitHub Issues for bug reports and feature requests
Discussions: Use GitHub Discussions for questions and ideas

This tool is designed to help improve Linux kernel code quality by identifying potential issues early in the development process. The analysis results should be used as guidance for improvement rather than definitive assessments of code quality.