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A C/C++ Code Vulnerability Dataset with Code Changes and CVE Summaries
Abstract
We collected a large C/C++ code vulnerability dataset from open-source Github projects, namely Big-Vul. We crawled the public Common Vulnerabilities and Exposures (CVE) database and CVE-related source code repositories. Specifically, we collected the descriptive information of the vulnerabilities from the CVE database, e.g., CVE IDs, CVE severity scores, and CVE summaries. With the CVE information and its related published Github code repository links, we downloaded all of the code repositories and extracted vulnerability related code changes. In total, Big-Vul contains 3,754 code vulnerabilities spanning 91 different vulnerability types. All these code vulnerabilities are extracted from 348 Github projects. All information is stored in the CSV format. We linked the code changes with the CVE descriptive information. Thus, our Big-Vul can be used for various research topics, e.g., detecting and fixing vulnerabilities, analyzing the vulnerability related code changes. Big-Vul is publicly available on Github.
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Cited by
Generative AI in cybersecurity: A comprehensive review of LLM applications and vulnerabilities
Ferrag, M. A., Alwahedi, F., Battah, A., Cherif, B., Mechri, A., Tihanyi, N., Bisztray, T., & Debbah, M. (2025) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2025.01.001 · Cited by 148
Is GitHub’s Copilot as bad as humans at introducing vulnerabilities in code?
Asare, O., Nagappan, M., & Asokan, N. (2023) · Empirical Software Engineering · doi:10.1007/s10664-023-10380-1 · Cited by 128
Siddiq, M. L., & Santos, J. C. S. (2022) · Proceedings of the 1st International Workshop on Mining Software Repositories Applications for Privacy and Security · doi:10.1145/3549035.3561184 · Cited by 101
Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities
Khare, A., Dutta, S., Li, Z., Solko-Breslin, A., Alur, R., & Naik, M. (2023) · arXiv · doi:10.48550/arxiv.2311.16169 · Cited by 64
Enhanced automated code vulnerability repair using large language models
de-Fitero-Dominguez, D., Garcia-Lopez, E., Garcia-Cabot, A., & Martinez-Herraiz, J.-J. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109291 · Cited by 43
Metadata
- Authors
- Fan, J., Li, Y., Wang, S., & Nguyen, T. N.
- Venue
- Proceedings of the 17th International Conference on Mining Software Repositories
- Publisher
- ACM
- Published
- 2020-06-29
- Pages
- 508-512
- OpenAlex
- W3091588759
- Cited by
- 498
- References
- 0
- FWCI
- 19.65
- Type
- conference paper
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
