Papers by Qianjin Du

2 papers
Code Vulnerability Detection via Nearest Neighbor Mechanism (2022.findings-emnlp)

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Challenge: Existing methods to learn code semantics from source code are difficult to identify.
Approach: They propose a method which retrieves multiple neighbor samples and utilizes label information to provide help for model predictions.
Outcome: Extensive experiments show that the proposed method can achieve obvious performance improvements compared to baseline models.
Joint Geometrical and Statistical Domain Adaptation for Cross-domain Code Vulnerability Detection (2023.emnlp-main)

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Challenge: Existing approaches to detect code vulnerability are limited by labeled training data on target domains.
Approach: They propose a cross-domain code vulnerability detection framework called MNCRI . they propose mutual nearest neighbor contrastive learning to align the source and target domains .
Outcome: The proposed framework outperforms state-of-the-art methods in cross-domain code vulnerability detection tasks.

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