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Effective Monotone Knowledge Integration in Kernel Support Vector Machines Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Mining Source Code Topics Through Topic Model and Words Embedding

Authors : Wei Emma Zhang, Quan Z. Sheng, Ermyas Abebe, M. Ali Babar, Andi Zhou

Published in: Advanced Data Mining and Applications

Publisher: Springer International Publishing

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Abstract

Developers nowadays can leverage existing systems to build their own applications. However, a lack of documentation hinders the process of software system reuse. We examine the problem of mining topics (i.e., topic extraction) from source code, which can facilitate the comprehension of the software systems. We propose a topic extraction method, Embedded Topic Extraction (EmbTE), that considers word semantics, which are never considered in mining topics from source code, by leveraging word embedding techniques. We also adopt Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF) to extract topics from source code. Moreover, an automated term selection algorithm is proposed to identify the most contributory terms from source code for the topic extraction task. The empirical studies on Github (https://​github.​com/​) Java projects show that EmbTE outperforms other methods in terms of providing more coherent topics. The results also indicate that method name, method comments, class names and class comments are the most contributory types of terms to source code topic extraction.

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Footnotes
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Metadata
Title
Mining Source Code Topics Through Topic Model and Words Embedding
Authors
Wei Emma Zhang
Quan Z. Sheng
Ermyas Abebe
M. Ali Babar
Andi Zhou
Copyright Year
2016
Book
Advanced Data Mining and Applications

Print ISBN: 978-3-319-49585-9

Electronic ISBN: 978-3-319-49586-6

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-49586-6_47

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