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The online programing services, such as Github,TopCoder, and EduCoder, havepromoted a lot of social interactions among the service users. However, theexisting social interactions is rather limited and inefficient due to the rapidincreasing of source-code repositories, which is difficult to explore manually.The emergence of source-code mining provides a promising way to analyze thosesource codes, so that those source codes can be relatively easy to understandand share among those service users. Among all the source-code miningattempts,program classification lays a foundation for various tasks related tosource-code understanding, because it is impossible for a machine to understanda computer program if it cannot classify the program correctly. Althoughnumerous machine learning models, such as the Natural Language Processing (NLP)based models and the Abstract Syntax Tree (AST) based models, have beenproposed to classify computer programs based on their corresponding sourcecodes, the existing works cannot fully characterize the source codes from theperspective of both the syntax and semantic information. To address thisproblem, we proposed a Graph Neural Network (GNN) based model, which integratesdata flow and function call information to the AST,and applies an improved GNNmodel to the integrated graph, so as to achieve the state-of-art programclassification accuracy. The experiment results have shown that the proposedwork can classify programs with accuracy over 97%.

Program Classification Using Gated Graph Attention Neural Network for Online Programming Service