Automatically Generating Code Comment Using Heterogeneous Graph Neural Networks

Abstract

Code summarization aims to generate readable summaries that describe the functionality of source code pieces. The main purpose of the code summarization is to help software developers understand the code and save their precious time. However, since programming languages are highly structured, it is challenging to generate high-quality code summaries. For this reason, this paper proposes a new approach named CCHG to automatically generate code comments. Compared to recent models that use additional information such as Abstract Syntax Trees as input, our proposed method only uses the most original code as input. We believe that programming languages are the same as natural languages. Each line of code is equivalent to a sentence, representing an independent meaning. Therefore, we split the entire code snippet into several sentence-level code. Coupled with token-level code, there are two types of code that need to be processed. So we propose heterogeneous graph networks to process the sentence-level and token-level code. Even though we do not introduce additional structural knowledge, the experimental results show that our model has a considerable performance, which indicates that our model can fully learn structural information and sequence information from code snippets.