
The Replication Package of "Today's cat is tomorrow's dog: accounting for time-based changes in the labels of ML vulnerability detection approaches" Part 1 (NVD Vuldeepecker Dataset) This repository includes zip files: Code.zip that contains the codes to replicate some parts of this study:a. 1_generate_datasets implements our methodology to generate the datasets.b. 2_run_models runs the ML models during the evaluation.c. 3_result_replication generates charts presented in the paper from the ML evaluation results. Datasets.zip that contain 2 folders:a. original datasets: 1 from NVD Vuldeepecker and 3 extracted from BigVul.b. NVD Vuldeepecker datasets: train, validation, test sets for each time of observation extracted using our methodology from NVD Vuldeepecker dataset. Pretrained-models.zip that we generated during our evaluation (3 test results for each time point in the timeline [2008-2019]). Results.zip of our evaluation, the folder ALL contains the overall results and other folders are results by model. Documentations INSTALL.pdf : how to install the codes README.pdf: readme file REQUIREMENTS.pdf: hardware and software requirements STATUS.pdf : status for artifact submission LICENSE.pdf: the license of this artifact PAPER.pdf: the camera-ready version of the paper Please refer to the following repositories for the other datasets and pre-trained models:- Part 2 LINUX : https://doi.org/10.5281/zenodo.10960662- Part 3 OPENSSL : https://doi.org/10.5281/zenodo.10966117- Part 4 POPPLER : https://doi.org/10.5281/zenodo.14713143
Machine Learning, Software security, Retrospective-Perspective, Dataset tuning
Machine Learning, Software security, Retrospective-Perspective, Dataset tuning
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