  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. The first
approaches to use structural information flattened the AST into a sequence.
Recently, more complex approaches based on random AST paths or graph neural
networks have improved on the models using flattened 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. Therefore, in this
paper, we present an approach that uses a graph-based neural architecture that
better 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 software
engineering literature, and two from machine learning literature.
