June 19, 2024, 4:20 a.m. | Ravil Mussabayev

cs.CR updates on arXiv.org arxiv.org

arXiv:2307.11454v2 Announce Type: replace
Abstract: This study explores the effectiveness of graph neural networks (GNNs) for vulnerability detection in software code, utilizing a real-world dataset of Java vulnerability-fixing commits. The dataset's structure, based on the number of modified methods in each commit, offers a natural partition that facilitates diverse investigative scenarios. The primary focus is to evaluate the general applicability of GNNs in identifying vulnerable code segments and distinguishing these from their fixed versions, as well as from …

analysis arxiv aware code code vulnerability commit cs.cr dataset detection graph java natural networks neural networks real software software code structure study vulnerability vulnerability analysis vulnerability detection world

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