Multi-View Pre-Trained Model for Code Vulnerability Identification

• URL: http://arxiv.org/abs/2208.05227v1
• Date: Wed, 10 Aug 2022 09:00:58 GMT
• Title: Multi-View Pre-Trained Model for Code Vulnerability Identification
• Authors: Xuxiang Jiang, Yinhao Xiao, Jun Wang, Wei Zhang
• Abstract summary: We propose a novel Multi-View Pre-Trained Model (MV-PTM) that encodes both sequential and multi-type structural information of the source code. Experiments conducted on two public datasets demonstrate the superiority of MV-PTM.
• Score: 10.129948567398506
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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