Conference proceeding
Automatically Identifying Bug Reports with Tactical Vulnerabilities by Deep Feature Learning
2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE), pp 333-344
Oct 2021
Abstract
Identifying and fixing bug reports with tactical vul-nerabilities in a timely and accurate manner is essential to ensure the security of the software architecture. Manually identifying the bug reports with tactical vulnerabilities is labor-intensive and challenging. This paper presents Itactivul, an approach to automatically identify bug reports with tactical vulnerabilities and recommend their tactical categories to guide the fix. Unlike the existing security bug report prediction approach, we are the first attempt to use deep learning to mine discriminative tactical text features only from the vulnerability descriptions of the National Vulnerability Database (NVD) and apply them to identify bug reports with tactical vulnerabilities. We evaluate Itactivul on three bug reports datasets gathered from three large-scale open-source projects, including Chromium, PHP, and Thunderbird. The experimental results show that Itactivul outperforms baselines by an average of 8.88 %, 13.58 %, and 6.61 % in the F1-score of three datasets, respectively. To improve the explainability of the features mined by Itactivul, we manually analyze the high-weight phrases extracted by using attention backtracking. The results show that Itactivul can mine key and potential tactical vulnerabilities text features.
Metrics
Details
- Title
- Automatically Identifying Bug Reports with Tactical Vulnerabilities by Deep Feature Learning
- Creators
- Wei Zheng - School of Software, Northwestern Polytechnical University,ChinaManqing Zhang - School of Software, Northwestern Polytechnical University,ChinaHui Tang - School of Software, Northwestern Polytechnical University,ChinaYuanfang Cai - Drexel UniversityXiang Chen - School of Information Science and Technology, Nantong University,ChinaXiaoxue Wu - School of Information Engineering, Yangzhou University,ChinaAbubakar Omari Abdallah Semasaba - School of Software, Northwestern Polytechnical University,China
- Publication Details
- 2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE), pp 333-344
- Conference
- 2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE), 32nd
- Publisher
- IEEE
- Number of pages
- 1
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000783962100030
- Scopus ID
- 2-s2.0-85126391221
- Other Identifier
- 991019167427404721
InCites Highlights
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Software Engineering
- Engineering, Electrical & Electronic