Detecting Security Patches via Behavioral Data in Code Repositories
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Abstract (arXiv)
The absolute majority of software today is developed collaboratively using collaborative version control tools such as Git. It is a common practice that once a vulnerability is detected and fixed, the developers behind the software issue a Common Vulnerabilities and Exposures or CVE record to alert the user community of the security hazard and urge them to integrate the security patch. However, some companies might not disclose their vulnerabilities and just update their repository. As a result, users are unaware of the vulnerability and may remain exposed. In this paper, we present a system to automatically identify security patches using only the developer behavior in the Git repository without analyzing the code itself or the remarks that accompanied the fix (commit message). We showed we can reveal concealed security patches with an accuracy of 88.3% and F1 Score of 89.8%. This is the first time that a language-oblivious solution for this problem is presented.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 14 references that are in the map. First posted on arXiv 4 Feb 2023.
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References · 6
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
- 2021SPI: Automated Identification of Security Patches via Commitscited by 19
- 2021PatchRNN: A Deep Learning-Based System for Security Patch Identificationcited by 13
- 2021DeepCVA: Automated Commit-level Vulnerability Assessment with Deep Multi-task Learningcited by 11
- 2018Identifying Unmaintained Projects in GitHubcited by 5
- 2019PHANTOM: Curating GitHub for engineered software projects using time-series clusteringcited by 3
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