Applying topic modeling and similarity for predicting bug severity in cross projects

Geunseok Yang, Kyeongsic Min, Jung Won Lee, Byungjeong Lee

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageEnglish
Pages (from-to)1583-1598
Number of pages16
JournalKSII Transactions on Internet and Information Systems
Issue number3
StatePublished - 31 Mar 2019


  • Bug Report
  • Bug Severity Prediction
  • Cross Projects
  • KL-Divergence
  • Topic Modeling


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