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Cited by 621

The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.

Deep code comment generation

Hu, Xing, Li, Ge, Xia, Xin, Lo, David, Jin, Zhi·2018·Proceedings of the 26th Conference on Program Comprehension
DOI 10.1145/3196321.3196334
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Abstract

During software maintenance, 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 Unfortunately, these comments are often mismatched, missing or outdated in the software projects.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 generated comments aim to help developers understand the functionality of Java methods. DeepCom applies Natural Language Processing (NLP) techniques to learn from a large code corpus and generates comments from learned features. We use a deep neural network that analyzes structural information of Java methods for better comments generation. We conduct experiments on a large-scale Java corpus built from 9,714 open source projects from GitHub. We evaluate the experimental results on a machine translation metric. Experimental results demonstrate that our method DeepCom outperforms the state-of-the-art by a substantial margin.

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Citations

Cites

Research works printed but not linked: 36

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Cited by

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Metadata

Authors
Hu, X., Li, G., Xia, X., Lo, D., & Jin, Z.
Venue
Proceedings of the 26th Conference on Program Comprehension
Publisher
ACM
Published
2018-05-28
Pages
200-210
DOI
10.1145/3196321.3196334
OpenAlex
W2884276923
Cited by
621
References
12
FWCI
49.83
Type
conference paper
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Funders
  • National Natural Science Foundation of China (61232015,61620106007)
  • National Basic Research Program of China (the 973 Program) (2015CB352201)

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