G-DCS: GCN-Based Deep Code Summary Generation Model

Abstract

In software engineering, software personnel faced many large-scale software and complex systems, these need programmers to quickly and accurately read and understand the code, and efficiently complete the tasks of software change or maintenance tasks. Code-NN is the first model to use deep learning to accomplish the task of code summary generation, but it is not used the structural information in the code itself. In the past five years, researchers have designed different code summarization systems based on neural networks. They generally use the end-to-end neural machine translation framework, but many current research methods do not make full use of the structural information of the code. This paper raises a new model called G-DCS to automatically generate a summary of java code; the generated summary is designed to help programmers quickly comprehend the effect of java methods. G-DCS uses natural language processing technology, and training the model uses a code corpus. This model could generate code summaries directly from the code files in the coded corpus. Compared with the traditional method, it uses the information of structural on the code. Through Graph Convolutional Neural Network (GCN) extracts the structural information on the code to generate the code sequence, which makes the generated code summary more accurate. The corpus used for training was obtained from GitHub. Evaluation criteria using BLEU-n. Experimental results show that our approach outperforms models that do not utilize code structure information.