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VulDetectArtifact

Artifact for TOSEM paper: Beyond Fidelity: Explaining Vulnerability Localization of Learning-based Detectors.

1.Datasets

For SARD dataset we have uploaded to zenodo, for Fan dataset, the related information is at MSR_20_Code_vulnerability_CSV_Dataset, the dataset csv can be downloaded from google driver. We extract func_before and func_after from it.

2.Preprocess Pipeline

For preprocess code into graph, please refer to preprocess/ReadMe.md

3.Pretrain embedding model

Run python pretrain.py detector_name path2train_datas embedding_model_path

  • detector_name: The name of detectors, choice is reveal, devign, ivdetect, deepwukong, we will soon add remaining 3 sequence-based detectors into this pipeline.

  • path2train_datas: The dir which stores train_vul.json, train_normal.json, eval_vul.json, eval_normal.json, test_vul.json, test_normal.json, the script will read training data from train jsons.

  • embedding_model_path: The path to the saved embedding model.

4.Detection Pipeline

Run python detection.py <args> to train detectors. <args> includes:

  • --detector <detector_name>, <detector_name> could be one of ["deepwukong", "reveal", "ivdetect", "devign", "tokenlstm", "vuldeepecker", "sysevr"]

  • --w2v_model_path <model_path>, <model_path> could be relative or absolute path of pretrained word2vec model.

  • --dataset_dir <dataset_dir>, <dataset_dir> is path to the dir storing json datas. It should include train_vul.json, train_normal.json, eval_vul.json, eval_normal.json, test_vul.json, test_normal.json.

  • --model_dir <model_dir>, <model_dir> is where the model pth file placed, it's corresponding directory. The scripts will automatically load the best model in the dir.

  • --train, means will train model. If there exist a model in <model_dir>, the script will first load that model and then train.

  • --test, means will test the model. There must be a model in <model_dir> first.

5.Explanation Pipeline

Run python explain.py <args>. <args> includes:

  • --detector <detector_name>, <detector_name> could be one of ["deepwukong", "reveal", "ivdetect", "devign", "tokenlstm", "vuldeepecker", "sysevr"]

  • --w2v_model_path <model_path>, <model_path> could be relative or absolute path of pretrained word2vec model.

  • --dataset_dir <dataset_dir>, <dataset_dir> is path to the dir storing json datas. It should include test_vul.json.

  • --model_dir <model_dir>, <model_dir> is where the model pth file placed, it's corresponding directory. The scripts will automatically load the best model in the dir.

  • --explainer <explainer_name>, <explainer_name> could be one of ["gnnexplainer", "pgexplainer", "gnnlrp", "gradcam", "deeplift"] for now. We are organizing the code in sequence-based explainers into this pipeline.

6.Citation

@misc{cheng2024fidelity,
      title={Beyond Fidelity: Explaining Vulnerability Localization of Learning-based Detectors}, 
      author={Baijun Cheng and Shengming Zhao and Kailong Wang and Meizhen Wang and Guangdong Bai and Ruitao Feng and Yao Guo and Lei Ma and Haoyu Wang},
      year={2024},
      eprint={2401.02686},
      archivePrefix={arXiv},
      primaryClass={cs.CR}
}