Automatic source code summarization is the task of generating natural language descriptions for source code. Automatic code summarization is a rapidly expanding research area, especially as the community has taken greater advantage of advances in neural network and AI technologies. In general, source code summarization techniques use the source code as input and outputs a natural language description. Yet a strong consensus is developing that using structural information as input leads to improved performance.e rst approaches to use structural information a ened the AST into a sequence. Recently, more complex approaches based on random AST paths or graph neural networks have improved on the models using a ened ASTs. However, the literature still does not describe the using a graph neural network together with source code sequence as separate inputs to a model. erefore, in this paper, we present an approach that uses a graph-based neural architecture that be er matches the default structure of the AST to generate these summaries. We evaluate our technique using a data set of 2.1 million Java method-comment pairs and show improvement over four baseline techniques, two from the so ware engineering literature, and two from machine learning literature.
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