Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2018

Abstract

Code summarization, aiming to generate succinctnatural language description of source code, is extremely useful for code search and code comprehension. It has played an important role in softwaremaintenance and evolution. Previous approachesgenerate summaries by retrieving summaries fromsimilar code snippets. However, these approachesheavily rely on whether similar code snippets canbe retrieved, how similar the snippets are, and failto capture the API knowledge in the source code,which carries vital information about the functionality of the source code. In this paper, we propose anovel approach, named TL-CodeSum, which successfully uses API knowledge learned in a different but related task to code summarization. Experiments on large-scale real-world industry Javaprojects indicate that our approach is effective andoutperforms the state-of-the-art in code summarization.

Discipline

Software Engineering

Research Areas

Data Science and Engineering

Publication

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelli-gence (IJCAI 2018), Stockholm, Sweden, 2018 July 13-19

First Page

2269

Last Page

2275

Identifier

10.24963/ijcai.2018/314

City or Country

Stockholm, Sweden

Additional URL

https://doi.org/10.24963/ijcai.2018/314

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