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FusionVul: A multimodal feature fusion framework for source code vulnerability detection
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DOI：10.1016/j.jss.2026.112992.png)
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Abstract
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• Develop a hybrid framework combining sequential syntactic and structural semantics representations via UniXcoder and GGNN. • Design a Cross-Attention Feature Fusion Network (CAFFNet) for explicit cross-modal interaction and fine-grained feature alignment. • Propose a Sample-Aware Weighting (SaW) strategy for multi-semantic integration at the prediction stage.
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