Code classification is a difficult issue in program understanding and automatic coding. Due to the elusive syntax and complicated semantics in programs, most existing studies use techniques based on abstract syntax tree (AST) and graph neural network (GNN) to create code representations for code classification. These techniques utilize the structure and semantic information of the code, but they only take into account pairwise associations and neglect the high-order correlations that already exist between nodes in the AST, which may result in the loss of code structural information. On the other hand, while a general hypergraph can encode high-order data correlations, it is homogeneous and undirected which will result in a lack of semantic and structural information such as node types, edge types, and directions between child nodes and parent nodes when modeling AST. In this study, we propose to represent AST as a heterogeneous directed hypergraph (HDHG) and process the graph by heterogeneous directed hypergraph neural network (HDHGN) for code classification. Our method improves code understanding and can represent high-order data correlations beyond paired interactions. We assess heterogeneous directed hypergraph neural network (HDHGN) on public datasets of Python and Java programs. Our method outperforms previous AST-based and GNN-based methods, which demonstrates the capability of our model.

翻译：代码分类是程序理解与自动编码中的难点问题。由于程序语法复杂且语义隐晦，现有研究多采用基于抽象语法树（AST）和图神经网络（GNN）的技术来生成代码分类所需的代码表征。这些技术利用了代码的结构与语义信息，但仅考虑了节点间的成对关联，忽视了AST中节点间存在的高阶关联性，可能导致代码结构信息丢失。另一方面，通用超图虽能编码高阶数据关联，但其同质且无向的特性在建模AST时会缺失节点类型、边类型以及子节点与父节点间方向等语义与结构信息。本研究提出将AST表示为异质有向超图（HDHG），并通过异质有向超图神经网络（HDHGN）进行代码分类。该方法改进了代码理解能力，能表征超越成对交互的高阶数据关联。我们在Python和Java程序的公开数据集上评估了异质有向超图神经网络（HDHGN），实验结果表明该方法优于以往基于AST和GNN的方法，验证了模型的有效性。