TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Applying Topic Modeling and Similarity for Predicting Bug Severity in Cross Projects |
| 영문제목(English Title) |
Applying Topic Modeling and Similarity for Predicting Bug Severity in Cross Projects |
| 저자(Author) |
Geunseok Yang
Kyeongsic Min
Jung-Won Lee
Byungjeong Lee
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| 원문수록처(Citation) |
VOL 13 NO. 03 PP. 1583 ~ 1598 (2019. 03) |
한글내용
(Korean Abstract) |
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영문내용
(English 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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| 키워드(Keyword) |
Bug Severity Prediction
Cross Projects
Topic Modeling
KL-Divergence
Bug Report
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| 파일첨부 |
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