Deep code comment generation with hybrid lexical and syntactical information

• Main Authors: HU, Xing, LI, Ge, XIA, Xin, LO, David, JIN, Zhi
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Comment generation; Deep learning; Program comprehension; Programming Languages and Compilers; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4407; https://ink.library.smu.edu.sg/context/sis_research/article/5410/viewcontent/Hu2019_Article_DeepCodeCommentGenerationWithH.pdf
• Institution: Singapore Management University

During software maintenance, developers spend a lot of time understanding the source code. Existing studies show that code comments help developers comprehend programs and reduce additional time spent on reading and navigating source code. Unfortunately, these comments are often mismatched, missing or outdated in software projects. Developers have to infer the functionality from the source code. This paper proposes a new approach named Hybrid-DeepCom to automatically generate code comments for the functional units of Java language, namely, Java methods. The generated comments aim to help developers understand the functionality of Java methods. Hybrid-DeepCom applies Natural Language Processing (NLP) techniques to learn from a large code corpus and generates comments from learned features. It formulates the comment generation task as the machine translation problem. Hybrid-DeepCom exploits a deep neural network that combines the lexical and structure information of Java methods for better comments generation. We conduct experiments on a large-scale Java corpus built from 9,714 open source projects on GitHub. We evaluate the experimental results on both machine translation metrics and information retrieval metrics. Experimental results demonstrate that our method Hybrid-DeepCom outperforms the state-of-the-art by a substantial margin. In addition, we evaluate the influence of out-of-vocabulary tokens on comment generation. The results show that reducing the out-of-vocabulary tokens improves the accuracy effectively.