@inproceedings{ab1722aea7904661bef16bc49a8e8af1,
title = "Multi-view Pre-trained Model for Code Vulnerability Identification",
abstract = "Vulnerability identification is crucial for cyber security in the software-related industry. Early identification methods require significant manual efforts in crafting features or annotating vulnerable code. Although the recent pre-trained models alleviate this issue, they overlook the multiple rich structural information contained in the code itself. In this paper, we propose a novel Multi-View Pre-Trained Model (MV-PTM) that encodes both sequential and multi-type structural information of the source code and uses contrastive learning to enhance code representations. The experiments conducted on two public datasets demonstrate the superiority of MV-PTM. In particular, MV-PTM improves GraphCodeBERT by 3.36\% on average in terms of F1 score.",
keywords = "Contrastive learning, Pre-trained model, Vulnerability identification",
author = "Xuxiang Jiang and Yinhao Xiao and Jun Wang and Wei Zhang",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 17th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2022 ; Conference date: 24-11-2022 Through 26-11-2022",
year = "2022",
doi = "10.1007/978-3-031-19211-1\_11",
language = "英语",
isbn = "9783031192104",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "127--135",
editor = "Lei Wang and Michael Segal and Jenhui Chen and Tie Qiu",
booktitle = "Wireless Algorithms, Systems, and Applications - 17th International Conference, WASA 2022, Proceedings",
address = "德国",
}