An emotion similarity based severity prediction of software bugs: A case study of open source projects

Geunseok Yang, Tao Zhang, Byungjeong Lee

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Many software development teams usually tend to focus on maintenance activities in general. Recently, many studies on bug severity prediction have been proposed to help a bug reporter determine severity. But they do not consider the reporter’s expression of emotion appearing in the bug report when they predict the bug severity level. In this paper, we propose a novel approach to severity prediction for reported bugs by using emotion similarity. First, we do not only compute an emotion-word probability vector by using smoothed unigram model (UM), but we also use the new bug report to find similar-emotion bug reports with Kullback–Leibler divergence (KL-divergence). Then, we introduce a new algorithm, Emotion Similarity (ES)-Multinomial, which modifies the original Naïve Bayes Multinomial algorithm. We train the model with emotion bug reports by using ES-Multinomial. Finally, we can predict the bug severity level in the new bug report. To compare the performance in bug severity prediction, we select related studies including Emotion Words-based Dictionary (EWD)-Multinomial, Naïve Bayes Multinomial, and another study as baseline approaches in open source projects (e.g., Eclipse, GNU, JBoss, Mozilla, and WireShark). The results show that our approach outperforms the baselines, and can reflect reporters’ emotional expressions during the bug reporting.

Original languageEnglish
Pages (from-to)2015-2026
Number of pages12
JournalIEICE Transactions on Information and Systems
Issue number8
StatePublished - Aug 2018


  • Bug report
  • Bug severity prediction
  • Emotion similarity
  • Software maintenance


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