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Type: Conference paper
Title: Mining source code topics through topic model and words embedding
Author: Zhang, W.
Sheng, Q.
Abebe, E.
Ali Babar, M.
Zhou, A.
Citation: Advanced Data Mining and Applications, 2016 / vol.10086 LNAI, pp.664-676
Publisher: Springer
Issue Date: 2016
ISBN: 9783319495859
ISSN: 0302-9743
Conference Name: International Conference on Advanced Data Mining and Applications (ADMA) (12 Dec 2016 - 15 Dec 2016 : Gold Coast, Qld)
Statement of
Wei Emma Zhang, Quan Z. Sheng, Ermyas Abebe, M. Ali Babar, and Andi Zhou
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 ( 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.
Keywords: Source code mining; Topic model; Word embedding
Description: LNCS, volume 10086
Rights: © Springer International Publishing AG 2016
RMID: 0030059762
DOI: 10.1007/978-3-319-49586-6_47
Appears in Collections:Computer Science publications

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