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Source code summaries are short natural language descriptions of codesnippets that help developers better understand and maintain source code. Therehas been a surge of work on automatic code summarization to reduce the burdenof writing summaries manually. However, most contemporary approaches mainlyleverage the information within the boundary of the method being summarized(i.e., local context), and ignore the broader context that could assist withcode summarization. This paper explores two global contexts, namely intra-classand inter-class contexts, and proposes the model CoCoSUM: Contextual CodeSummarization with Multi-Relational Graph Neural Networks. CoCoSUM firstincorporates class names as the intra-class context to generate the classsemantic embeddings. Then, relevant Unified Modeling Language (UML) classdiagrams are extracted as inter-class context and are encoded into the classrelational embeddings using a novel Multi-Relational Graph Neural Network(MRGNN). Class semantic embeddings and class relational embeddings, togetherwith the outputs from code token encoder and AST encoder, are passed to adecoder armed with a two-level attention mechanism to generate high-quality,context-aware code summaries. We conduct extensive experiments to evaluate ourapproach and compare it with other automatic code summarization models. Theexperimental results show that CoCoSUM is effective and outperformsstate-of-the-art methods. Our source code and experimental data are availablein the supplementary materials and will be made publicly available.

CoCoSum: Contextual Code Summarization with Multi-Relational Graph Neural Network