Publication Type

Journal Article

Version

acceptedVersion

Publication Date

10-2019

Abstract

Software developers have benefited from various sources of knowledge such as forums, question-and-answer sites, and social media platforms to help them in various tasks. Extracting software-related knowledge from different platforms involves many challenges. In this paper, we propose an approach to improve the effectiveness of knowledge extraction tasks by performing cross-platform analysis. Our approach is based on transfer representation learning and word embedding, leveraging information extracted from a source platform which contains rich domain-related content. The information extracted is then used to solve tasks in another platform (considered as target platform) with less domain-related content. We first build a word embedding model as a representation learned from the source platform, and use the model to improve the performance of knowledge extraction tasks in the target platform. We experiment with Software Engineering Stack Exchange and Stack Overflow as source platforms, and two different target platforms, i.e., Twitter and YouTube. Our experiments show that our approach improves performance of existing work for the tasks of identifying software-related tweets and helpful YouTube comments.

Keywords

Word Embeddings, Transfer Representation Learning, Software Engineering

Discipline

Programming Languages and Compilers | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Empirical Software Engineering

Volume

25

Issue

1

First Page

996

Last Page

1030

ISSN

1382-3256

Identifier

10.1007/s10664-019-09775-w

Publisher

Springer Verlag (Germany)

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/s10664-019-09775-w

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