{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/regvd-revisiting-graph-neural-networks-for-1","title":"ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection","arxiv_id":"2110.07317","date":"2021-10-14","proceeding":null,"authors":["Van-Anh Nguyen","Dai Quoc Nguyen","Van Nguyen","Trung Le","Quan Hung Tran","Dinh Phung"],"abstract":"Identifying vulnerabilities in the source code is essential to protect the software systems from cyber security attacks. It, however, is also a challenging step that requires specialized expertise in security and code representation. To this end, we aim to develop a general, practical, and programming language-independent model capable of running on various source codes and libraries without difficulty. Therefore, we consider vulnerability detection as an inductive text classification problem and propose ReGVD, a simple yet effective graph neural network-based model for the problem. In particular, ReGVD views each raw source code as a flat sequence of tokens to build a graph, wherein node features are initialized by only the token embedding layer of a pre-trained programming language (PL) model. ReGVD then leverages residual connection among GNN layers and examines a mixture of graph-level sum and max poolings to return a graph embedding for the source code. ReGVD outperforms the existing state-of-the-art models and obtains the highest accuracy on the real-world benchmark dataset from CodeXGLUE for vulnerability detection. Our code is available at: \\url{https://github.com/daiquocnguyen/GNN-ReGVD}.","url_abs":"https://arxiv.org/abs/2110.07317v3","url_pdf":"https://arxiv.org/pdf/2110.07317v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"regvd-revisiting-graph-neural-networks-for-1","repo_url":"https://github.com/daiquocnguyen/gnn-regvd","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"graph-embedding","task_name":"Graph Embedding"},{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"vulnerability-detection","task_name":"Vulnerability Detection"},{"task_slug":"text-classification-1","task_name":"text-classification"}],"methods":[{"method_slug":"codebert","method_name":"CodeBERT"},{"method_slug":"residual-connection","method_name":"Residual Connection"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}