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- W4320858865 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." @default.
- W4320858865 created "2023-02-16" @default.
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- W4320858865 date "2022-08-10" @default.
- W4320858865 modified "2023-09-29" @default.
- W4320858865 title "Multi-View Pre-Trained Model for Code Vulnerability Identification" @default.
- W4320858865 doi "https://doi.org/10.48550/arxiv.2208.05227" @default.
- W4320858865 hasPublicationYear "2022" @default.
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