• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Applying Topic Modeling and Similarity for Predicting Bug Severity in Cross Projects

Vol. 13, No.3, March 31, 2019
10.3837/tiis.2019.03.026, Download Paper (Free):

Abstract

Recently, software has increased in complexity and been applied in various industrial fields. As a result, the presence of software bugs cannot be avoided. Various bug severity prediction methodologies have been proposed, but their performance needs to be further improved. In this study, we propose a novel technique for bug severity prediction in cross projects such as Eclipse, Mozilla, WireShark, and Xamarin by using topic modeling and similarity (i.e., KL-divergence). First, we construct topic models from bug repositories in cross projects using Latent Dirichlet Allocation (LDA). Then, we find topics in each project that contain the most numerous similar bug reports by using a new bug report. Next, we extract the bug reports belonging to the selected topics and input them to a Naïve Bayes Multinomial (NBM) algorithm. Finally, we predict the bug severity in the new bug report. In order to evaluate the performance of our approach and to verify the difference between cross projects and single project, we compare it with the Naïve Bayes Multinomial approach; the Lamkanfi methodology, which is a well-known bug severity prediction approach; and an emotional similarity-based bug severity prediction approach. Our approach exhibits a better performance than the compared methods.


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Cite this article

[IEEE Style]
Geunseok Yang, Kyeongsic Min, Jung-Won Lee and Byungjeong Lee, "Applying Topic Modeling and Similarity for Predicting Bug Severity in Cross Projects," KSII Transactions on Internet and Information Systems, vol. 13, no. 3, pp. 1583-1598, 2019. DOI: 10.3837/tiis.2019.03.026

[ACM Style]
Yang, G., Min, K., Lee, J., and Lee, B. 2019. Applying Topic Modeling and Similarity for Predicting Bug Severity in Cross Projects. KSII Transactions on Internet and Information Systems, 13, 3, (2019), 1583-1598. DOI: 10.3837/tiis.2019.03.026