Automating the Detection of Code Vulnerabilities by Analyzing GitHub Issues
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866929668238606336 |
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| author | Cipollone, Daniele Wang, Changjie Scazzariello, Mariano Ferlin, Simone Izadi, Maliheh Kostic, Dejan Chiesa, Marco |
| author_facet | Cipollone, Daniele Wang, Changjie Scazzariello, Mariano Ferlin, Simone Izadi, Maliheh Kostic, Dejan Chiesa, Marco |
| contents | In today's digital landscape, the importance of timely and accurate vulnerability detection has significantly increased. This paper presents a novel approach that leverages transformer-based models and machine learning techniques to automate the identification of software vulnerabilities by analyzing GitHub issues. We introduce a new dataset specifically designed for classifying GitHub issues relevant to vulnerability detection. We then examine various classification techniques to determine their effectiveness. The results demonstrate the potential of this approach for real-world application in early vulnerability detection, which could substantially reduce the window of exploitation for software vulnerabilities. This research makes a key contribution to the field by providing a scalable and computationally efficient framework for automated detection, enabling the prevention of compromised software usage before official notifications. This work has the potential to enhance the security of open-source software ecosystems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_05258 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automating the Detection of Code Vulnerabilities by Analyzing GitHub Issues Cipollone, Daniele Wang, Changjie Scazzariello, Mariano Ferlin, Simone Izadi, Maliheh Kostic, Dejan Chiesa, Marco Software Engineering Artificial Intelligence Cryptography and Security D.2.5; K.6.3 In today's digital landscape, the importance of timely and accurate vulnerability detection has significantly increased. This paper presents a novel approach that leverages transformer-based models and machine learning techniques to automate the identification of software vulnerabilities by analyzing GitHub issues. We introduce a new dataset specifically designed for classifying GitHub issues relevant to vulnerability detection. We then examine various classification techniques to determine their effectiveness. The results demonstrate the potential of this approach for real-world application in early vulnerability detection, which could substantially reduce the window of exploitation for software vulnerabilities. This research makes a key contribution to the field by providing a scalable and computationally efficient framework for automated detection, enabling the prevention of compromised software usage before official notifications. This work has the potential to enhance the security of open-source software ecosystems. |
| title | Automating the Detection of Code Vulnerabilities by Analyzing GitHub Issues |
| topic | Software Engineering Artificial Intelligence Cryptography and Security D.2.5; K.6.3 |
| url | https://arxiv.org/abs/2501.05258 |