Code Language Models (codeLMs) and Graph Neural Networks (GNNs) are widely used in code vulnerability detection. However, a critical yet often overlooked issue is that GNNs primarily rely on aggregating information from adjacent nodes, limiting structural information transfer to single-layer updates. In code graphs, nodes and relationships typically require cross-layer information propagation to fully capture complex program logic and potential vulnerability patterns. Furthermore, while some studies utilize codeLMs to supplement GNNs with code semantic information, existing integration methods have not fully explored the potential of their collaborative effects.

中文翻译：

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Vul-LMGNN：融合语言模型和在线提炼的图神经网络进行代码漏洞检测


代码语言模型 （codeLM） 和图形神经网络 （GNN） 广泛用于代码漏洞检测。然而，一个关键但经常被忽视的问题是 GNN 主要依赖于聚合来自相邻节点的信息，从而限制了结构信息传输到单层更新。在代码图中，节点和关系通常需要跨层信息传播，以完全捕获复杂的程序逻辑和潜在的漏洞模式。此外，虽然一些研究利用 codeLMs 用代码语义信息补充 GNN，但现有的集成方法尚未充分探索其协作效应的潜力。