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See detailRevisiting the VCCFinder approach for the identification of vulnerability-contributing commits
Riom, Timothée UL; Sawadogo, Delwende Donald Arthur UL; Allix, Kevin UL et al

in Empirical Software Engineering (2021), 26

Detecting vulnerabilities in software is a constant race between development teams and potential attackers. While many static and dynamic approaches have focused on regularly analyzing the software in its ... [more ▼]

Detecting vulnerabilities in software is a constant race between development teams and potential attackers. While many static and dynamic approaches have focused on regularly analyzing the software in its entirety, a recent research direction has focused on the analysis of changes that are applied to the code. VCCFinder is a seminal approach in the literature that builds on machine learning to automatically detect whether an incoming commit will introduce some vulnerabilities. Given the influence of VCCFinder in the literature, we undertake an investigation into its performance as a state-of-the-art system. To that end, we propose to attempt a replication study on the VCCFinder supervised learning approach. The insights of our failure to replicate the results reported in the original publication informed the design of a new approach to identify vulnerability-contributing commits based on a semi-supervised learning technique with an alternate feature set. We provide all artefacts and a clear description of this approach as a new reproducible baseline for advancing research on machine learning-based identification of vulnerability-introducing commits [less ▲]

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See detailRevisiting the impact of common libraries for android-related investigations
Li, Li; Riom, Timothée UL; Bissyande, Tegawendé François D Assise UL et al

in Journal of Systems and Software (2019), 154

Detailed reference viewed: 46 (1 UL)