Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2018

Abstract

During software maintenance, code comments help developerscomprehend programs and reduce additional time spent on readingand navigating source code. Unfortunately, these comments areoften mismatched, missing or outdated in the software projects.Developers have to infer the functionality from the source code.This paper proposes a new approach named DeepCom to automatically generate code comments for Java methods. The generatedcomments aim to help developers understand the functionalityof Java methods. DeepCom applies Natural Language Processing(NLP) techniques to learn from a large code corpus and generatescomments from learned features. We use a deep neural networkthat analyzes structural information of Java methods for bettercomments generation. We conduct experiments on a large-scaleJava corpus built from 9,714 open source projects from GitHub. Weevaluate the experimental results on a machine translation metric. Experimental results demonstrate that our method DeepComoutperforms the state-of-the-art by a substantial margin.

Keywords

comment generation, deep learning, program comprehension

Discipline

Software Engineering

Research Areas

Data Science and Engineering

Publication

ICPC '18: Proceedings of the 26th Conference on Program Comprehension, Gothenburg, Sweden, May 27-28

First Page

200

Last Page

210

ISBN

9781450357142

Identifier

10.1145/3196321.3196334

Publisher

ACM

City or Country

New York

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/3196321.3196334

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