MatsVD: Boosting Statement-Level Vulnerability Detection via Dependency-Based Attention-Reference-Cited by-同舟云学术

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MatsVD: Boosting Statement-Level Vulnerability Detection via Dependency-Based Attention

Published:2024-07-24 Issue: Volume:3119 Page:115-124
ISSN:
Container-title:Proceedings of the 15th Asia-Pacific Symposium on Internetware
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Author:

Weng Cheng¹^ORCID,Qin Yihao¹^ORCID,Lin Bo¹^ORCID,Liu Pei¹^ORCID,Chen Liqian¹^ORCID

Affiliation:

1. National University of Defense Technology, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3671016.3674807

Reference35 articles.

1. Saikat Chakraborty, Rahul Krishna, Yangruibo Ding, and Baishakhi Ray. 2022. Deep Learning Based Vulnerability Detection: Are We There Yet?IEEE Transactions on Software Engineering 48, 9 (2022), 3280–3296.

2. Checkmarx. 2024. [Online]. Available: https://checkmarx.com/.

3. Measuring and Relieving the Over-Smoothing Problem for Graph Neural Networks from the Topological View

4. Cppcheck. 2024. [Online]. Available: https://cppcheck.sourceforge.io/.

5. Roland Croft, M. Ali Babar, and M. Mehdi Kholoosi. 2023. Data Quality for Software Vulnerability Datasets. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE). 121–133.

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