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Entropy 2017, 19(5), 173; doi:10.3390/e19050173

En-LDA: An Novel Approach to Automatic Bug Report Assignment with Entropy Optimized Latent Dirichlet Allocation

1,†,* , 1,†
and
2,†
1
Research Center on Data Sciences, Beijing University of Chemical Technology, Beijing 100039, China
2
School of Knowledge Science, Japan Advanced Institute of Science and Technology, 1-1 Ashahidai, Nomi, Ishikawa 923-1292, Japan
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Kevin H. Knuth
Received: 6 February 2017 / Revised: 13 April 2017 / Accepted: 14 April 2017 / Published: 25 April 2017
View Full-Text   |   Download PDF [362 KB, uploaded 25 April 2017]   |  

Abstract

With the increasing number of bug reports coming into the open bug repository, it is impossible to triage bug reports manually by software managers. This paper proposes a novel approach called En-LDA (Entropy optimized Latent Dirichlet Allocation (LDA)) for automatic bug report assignment. Specifically, we propose entropy to optimize the number of topics of the LDA model and further use the entropy optimized LDA to capture the expertise and interest of developers in bug resolution. A developer’s interest in a topic is modeled by the number of the developer’s comments on bug reports of the topic divided by the number of all the developer’s comments. A developer’s expertise in a topic is modeled by the number of the developer’s comments on bug reports of the topic divided by the number of all developers’ comments on the topic. Given a new bug report, En-LDA recommends a ranked list of developers who are potentially adequate to resolve the new bug. Experiments on Eclipse JDT and Mozilla Firefox projects show that En-LDA can achieve high recall up to 84% and 58%, and precision up to 28% and 41%, respectively, which indicates promising aspects of the proposed approach. View Full-Text
Keywords: automatic bug report assignment; bug resolution; entropy measure; Latent Dirichlet Allocation automatic bug report assignment; bug resolution; entropy measure; Latent Dirichlet Allocation
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Zhang, W.; Cui, Y.; Yoshida, T. En-LDA: An Novel Approach to Automatic Bug Report Assignment with Entropy Optimized Latent Dirichlet Allocation. Entropy 2017, 19, 173.

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