Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2020

Abstract

As the scale of software projects increases, the code comments are more and more important for program comprehension. Unfortunately, many code comments are missing, mismatched or outdated due to tight development schedule or other reasons. Automatic code comment generation is of great help for developers to comprehend source code and reduce their workload. Thus, we propose a code comment generation tool (DeepCommenter) to generate descriptive comments for Java methods. DeepCommenter formulates the comment generation task as a machine translation problem and exploits a deep neural network that combines the lexical and structural information of Java methods. We implement DeepCommenter in the form of an Integrated Development Environment (i.e., Intellij IDEA) plug-in. Such plug-in is built upon a Client/Server architecture. The client formats the code selected by the user, sends request to the server and inserts the comment generated by the server above the selected code. The server listens for client’s request, analyzes the requested code using the pre-trained model and sends back the generated comment to the client. The pre-trained model learns both the lexical and syntactical information from source code tokens and Abstract Syntax Trees (AST) respectively and combines these two types of information together to generate comments. To evaluate DeepCommenter, we conduct experiments on a large corpus built from a large number of open source Java projects on GitHub. The experimental results on different metrics show that DeepCommenter outperforms the state-of-the-art approaches by a substantial margin. Demo URL: https://youtu.be/acdH5X-eBw4; Plug-in download: https://git.io/JegwQ

Keywords

Comment generation, program comprehension, deep learning

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

ESEC/FSE '20: Proceedings of the 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering: Virtual, November 8-13

First Page

1571

Last Page

1575

ISBN

9781450370431

Identifier

10.1145/3368089.3417926

Publisher

ACM

City or Country

New York

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/3368089.3417926

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