Data Quality for Software Vulnerability Datasets-Reference-Cited by-同舟云学术

Data Quality for Software Vulnerability Datasets

Published:2023-05 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)
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Author:

Croft Roland¹,Babar M. Ali¹,Kholoosi M. Mehdi¹

Affiliation:

1. School of Computer Science, CREST, University of Adelaide,Australia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10172484/10172342/10172650.pdf?arnumber=10172650

Reference73 articles.

1. Noisy label learning for security defects

2. The impact of tangled code changes

3. Finding A Needle in a Haystack: Automated Mining of Silent Vulnerability Fixes

4. Using thematic analysis in psychology

5. Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks;zhou;Advances in neural information processing systems,2019

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2. Beyond Fidelity: Explaining Vulnerability Localization of Learning-based Detectors;ACM Transactions on Software Engineering and Methodology;2024-01-31

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4. DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection;Proceedings of the 26th International Symposium on Research in Attacks, Intrusions and Defenses;2023-10-16

5. Empirical Analysis of Software Vulnerabilities Causing Timing Side Channels;2023 IEEE Conference on Communications and Network Security (CNS);2023-10-02