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9,302 results for “DETECT”
Dataset, models and code for "Automating global landslide detection with heterogeneous ensemble deep-learning classification"
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Towards Human-interpretable Explanation in Code Clone Detection using LLM-based Post Hoc Explainer
<h2><strong>Overview</strong></h2> <div>This artifact accompanies the APSEC 2025 Research Track submission on <em>Towards Human-interpretable Explanation in Code Clone Detection using LLM-based Post Hoc Explainer</em>. The research focuses on using Large Language Models (LLMs) with local neighborhood sampling with in-context learning to provide explanations for GraphCodeBERT's code clone detection predictions.</div> <p> </p> <h2>Artifact Contents</h2> <h3>1. Core Implementation</h3> <div> <ul> <li> GraphCodeBERT_CloneDetection.ipynb: Complete Jupyter notebook containing: <ul> <li>GraphCodeBERT model setup and training</li> <li>Code clone detection experiments on BigCloneBench and Google Code Jam datasets</li> <li>In-Context Learning prompt generation for explanation</li> <li>LLM-based explanation pipeline</li> </ul> </li> </ul> </div> <h3>2. In-Context Learning Prompts (ICL_Prompts)</h3> <div>The ICL_Prompts directory contains structured prompts for 10 different Google Code Jam pairs (data_GoogleCodeJam_Pair1 through data_GoogleCodeJam_Pair10) used in the manual validation step, each with varying context lengths:</div> <ul> <li>layer_size4.txt: prompts with 4 examples</li> <li>layer_size8.txt: prompts with 8 examples</li> <li>layer_size16.txt: prompts with 16 examples</li> <li>layer_size32.txt: prompts with 32 examples</li> <li>layer_size48.txt: prompts with 48 examples</li> <li>layer_size64.txt: prompts with 64 examples</li> </ul> <div>Each prompt file is based on the prompt template structure:</div> <ul> <li><strong>Context:</strong> Description of GraphCodeBERT model</li> <li><strong>Dataset:</strong> Examples with code pairs, confidence scores, and predictions</li> <li><strong>Question:</strong> A query asking for explanation of a specific code pair</li> <li><strong>Instructions</strong> for generating explanations</li> </ul> <h3>3. Validation Results</h3> <div> <ul> <li>Manual_Validation_Results.xlsx: Manual evaluation results of generated explanations including quality assessments and human annotations.</li> </ul> </div> <h2>Usage Instructions</h2> <h3>Prerequisites</h3> <div><code>pip install torch transformers tree_sitter scikit-learn</code></div> <div> </div> <h3>Running the Code</h3> <ol> <li>Open `GraphCodeBERT_CloneDetection.ipynb` in Jupyter/Colab</li> <li>Follow the notebook cells to: <ol> <li>Set up the GraphCodeBERT model</li> <li>Load and preprocess datasets</li> <li>Run clone detection experiments</li> <li>Generate prompts for explanation</li> </ol> </li> </ol> <h3>File Organization</h3> <div><code>├── GraphCodeBERT_CloneDetection.ipynb # Main implementation</code></div> <div><code>├── Manual_Validation_Results.xlsx # Human evaluation results</code></div> <div><code>├── ICL_Prompts/ # Generated prompts</code></div> <div><code>│ ├── data_GoogleCodeJam_Pair1/ # Pair 1 prompts</code></div> <div><code>│ │ ├── layer_size4.txt # 4-example prompts</code></div> <div><code>│ │ ├── layer_size8.txt # 8-example prompts</code></div> <div><code>│ │ └── ... # Other sizes</code></div> <div><code>│ ├── data_GoogleCodeJam_Pair2/ # Pair 2 prompts</code></div> <div><code>│ └── ... # Additional pairs</code></div> <div><code>└── README.md # This file</code></div> <h3>Citation</h3> <div>Please cite this artefact using the DOI provided by Zenodo.</div> <h3>Contact</h3> <div>For questions about this artifact or research, please contact the authors [to be updated due to double-blind policy].</div>
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