Artifacts of the paper under review by ISSTA

Artifacts of the paper under review by ISSTA Welcome to the online repository of the ISSTA 2024 Submission 1305. We release the source code, dataset, and outputs of our study. Getting Started Requirements To run the project, please ensure that you have the following dependencies installed: pytorch=2.0.0; torchvision=0.15.1; torchaudio; datasets==1.16.1; transformers==4.21.1; tensorboard==2.12.2; tree-sitter==0.19.1; nltk=3.8.1; scipy=1.10.1; You can either install these requirements manually or execute the following script: bash scripts/setup.sh Dataset We perform our experiments on following datasets: * CodeSearchNet from [here](https://github.com/github/CodeSearchNet). * BFP dataset from Tufano et al.'s TOSEM'19 paper. * Defects4J dataset from René et al.'s ISSTA'14 paper. * VulRD from Fu et al.'s FSE'22 paper. Download the pre-training dataset: python Dataset\pre-training\download.py Pretrain the model bash scripts/pre-train.sh -g [GPU_ID] Task 1: bug repair bash scripts/finetune_d4j.sh -g [GPU_ID] If the evaluation during fine-tuning takes too long, you can adjust the "--evaluate_sample_size" parameter. This parameter refers to the number of cases in the validation set during evaluation. To evaluate the performance of a specific checkpoint, add the flag "-e" followed by the checkpoint path. Task 2: vulnerability repair bash scripts/finetune_vul.sh -g [GPU_ID] If the evaluation during fine-tuning takes too long, you can adjust the "--evaluate_sample_size" parameter. This parameter refers to the number of cases in the validation set during evaluation. To evaluate the performance of a specific checkpoint, add the flag "-e" followed by the checkpoint path. Outputs The outputs of our tool are stored in the `outputs` folder.