研究論文の公式実装およびアーティファクト：
「DVDR-LLM: ソフトウェア脆弱性の検出と修復において、LLMはいつ協調するとより優れているのか？」
DVDR-LLMは、ソフトウェア脆弱性の検出と修復において複数の大規模言語モデル（LLM）を統合する際の根本的なトレードオフを体系的に検証するアンサンブルフレームワークです。我々の包括的な評価は、セキュリティ上重要なアプリケーションにおける適合率-再現率のバランス、モデル多様性の利点、コンセンサスベースのアプローチに関する重要な洞察を明らかにします。
DVDR_LLM/
├── dvdr_llm/ # Main package (professional structure)
│ ├── __init__.py # Package exports
│ ├── cli.py # Command-line interface
│ ├── config.py # Configuration settings
│ ├── core/ # Core functionality
│ │ ├── api_client.py # LLM API communication
│ │ ├── detector.py # VulnerabilityDetector class
│ │ └── prompts.py # Prompt generation utilities
│ ├── analysis/ # Analysis modules
│ │ ├── consensus.py # Consensus analysis
│ │ └── metrics.py # Performance metrics
│ ├── evaluation/ # Model evaluation
│ │ └── evaluator.py # ModelEvaluator class
│ ├── visualization/ # Plotting and visualization
│ │ └── plotter.py # ResultsPlotter class
│ └── tools/ # Additional utilities
├── utils/ # Original utility modules
│ ├── api.py # LLM API interaction helpers
│ ├── database.py # SQLite helper functions
│ └── config.py # Configuration constants
├── data/ # Datasets and databases
│ ├── vulnerabilities.csv # Vulnerability data
│ └── vulnerable and patched codes.sqlite
├── output/ # Generated results and databases
│ ├── database_*.sqlite # Model-specific databases
│ ├── consensus_analysis/ # Consensus analysis results
│ ├── metrics/ # Performance metrics
│ └── database_exports/ # Exported data
├── examples/ # Usage examples
│ └── basic_usage.py # Basic usage demonstration
├── docs/ # Documentation
│ └── README_consensus_analysis.md
├── paper/ # Research paper
│ ├── main.pdf # Published paper
│ └── main.tex # LaTeX source
├── setup.py # Package installation
├── requirements.txt # Dependencies
├── CHANGELOG.md # Change log
├── LICENSE # MIT License
└── README.md # This file
リポジトリをクローン：
git clone https://github.com/Erroristotle/DVDR_LLM.git
cd DVDR_LLM
依存関係をインストール：
pip install -r requirements.txt
pip install -e . # Install package in development mode
インストールを確認：
python verify_package.py
# Run vulnerability detection with ensemble
dvdr-llm detect --models llama3-8b,codellama-7b --database data/vulnerabilities.sqlite
# Analyze consensus patterns (RQ2)
dvdr-llm analyze consensus --input results/metrics/model_predictions.csv --threshold 0.6
# Generate conflict pattern visualization (Figure in paper)
python dvdr_llm/visualization/conflict_pattern_plot.py results/metrics/reviewer_insights_disagreement_patterns.csv -o reviewer_insights
# Evaluate ensemble performance across abstraction levels (RQ3)
dvdr-llm evaluate --models-dir output/ --abstraction-analysis
from dvdr_llm import VulnerabilityDetector, ConsensusAnalyzer, ModelEvaluator
# Initialize vulnerability detector
detector = VulnerabilityDetector("llama3-8b-instruct")
# Connect to database
detector.connect_to_database("data/vulnerabilities.sqlite")
# Analyze code for vulnerabilities
code = """
void vulnerable_function(char *input) {
char buffer[100];
strcpy(buffer, input); // Buffer overflow
}
"""
# Get CVE predictions
cve_names = detector.analyze_code_for_cves(code, 2023)
print(f"Identified CVEs: {cve_names}")
# Detect vulnerability
is_vulnerable = detector.detect_vulnerability(code)
print(f"Is vulnerable: {is_vulnerable}")
# Process database entries
stats = detector.process_database_entries(limit=100)
print(f"Processed {stats['processed']} entries")
# Clean up
detector.close()
# Multi-model consensus analysis
model_databases = {
"llama3-8b": "output/database_llama3-8b-instruct.sqlite",
"codellama-7b": "output/database_codellama-7b-instruct.sqlite",
"gemma2-9b": "output/database_gemma2-9b.sqlite"
}
analyzer = ConsensusAnalyzer()
consensus_results = analyzer.run_full_analysis(model_databases)
# Model evaluation
evaluator = ModelEvaluator()
evaluation_results = evaluator.evaluate_multiple_models(
model_databases,
"data/ground_truth.csv"
)
# Generate Table 2 (Model comparison with delta percentages)
python dvdr_llm/tools/model_contribution_analysis.py results/metrics/model_predictions.csv --compare-ensemble --threshold 0.7
# Analyze individual vs ensemble performance
dvdr-llm evaluate --comparison-analysis --threshold 0.7
# Generate Figure 3 (Threshold sensitivity analysis)
python dvdr_llm/tools/threshold_analysis.py results/metrics/model_predictions.csv --range 0.3-0.8
# Statistical validation of threshold selection
python dvdr_llm/analysis/statistical_validation.py --threshold-analysis
# Generate Table 3 (Abstraction level analysis)
python dvdr_llm/evaluation/evaluator.py --abstraction-analysis --threshold 0.6
# Level-specific performance evaluation
dvdr-llm evaluate --abstraction-levels 1,2,3 --ensemble
# Analyze patch quality metrics (ROUGE, CodeBLEU, Complexity)
python dvdr_llm/tools/patch_quality_analysis.py results/patches/ --weighted-scoring
# Generate Figure 4 (Patch similarity and complexity analysis)
dvdr-llm visualize --patch-analysis --metrics results/patch_metrics.csv
# Main conflict pattern analysis figure (Figure 2)
python dvdr_llm/visualization/conflict_pattern_plot.py results/metrics/reviewer_insights_disagreement_patterns.csv -o reviewer_insights
# Threshold sensitivity analysis (Figure 3)
python dvdr_llm/visualization/enhanced_figure3_generator.py results/metrics/model_predictions.csv
# Statistical validation plots (Appendix)
python dvdr_llm/analysis/statistical_significance_analysis.py --generate-plots
dvdr_llm/config.pyを編集してLLMエンドポイントを設定します：
LLM_MODELS = {
"llama3_8b": "ollama run llama3:8b-instruct",
"codellama_7b": "ollama run codellama:7b-instruct",
"mistral_7b": "ollama run mistral:7b-instruct",
# Add your model configurations
}
# Consensus thresholds for different scenarios
CONSENSUS_THRESHOLDS = {
"conservative": 0.8, # High precision, low false positives
"balanced": 0.6, # Balanced precision-recall
"sensitive": 0.4 # High recall, catch more vulnerabilities
}
このフレームワークは再現性のための標準化されたプロンプトテンプレートを使用します：