A Machine Learning Approach for Classifying the Default Bug Severity Level

Abdalrahman Aburakhia, Mohammad Alshayeb*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Bug reports (BRs) play a major role in the software maintenance process; they alert developers about the bugs discovered by the end-users. Software applications utilize bug tracking systems (BTS) to manage submitted bug reports. Recent studies showed that the majority of BRs in BTS belong to the default severity category, which does not represent their actual severity. In this paper, we propose an approach that can automatically classify default bug reports into severe or non-severe categories. We curated a dataset based on the history of bug report logs. After that, we used the Support Vector Machine algorithm and Term Frequency-Inverse Document Frequency feature extraction method to classify default bug reports into severe or non-severe categories. The results show that building customized models for default severity bug reports provides better and more reliable results than training one model for all severity. Overall, the proposed Log model outperformed the three models (approaches) from the literature; it achieved an improvement of up to ~ 4% f-measure compared to others, and in some projects, it achieved an improvement of 11.2% f-measure. Moreover, we investigated the impact of sentiment analysis on default bug severity prediction; the results show no noticeable influence.

Original languageEnglish
Pages (from-to)13131-13148
Number of pages18
JournalArabian Journal for Science and Engineering
Volume49
Issue number9
DOIs
StatePublished - Sep 2024

Bibliographical note

Publisher Copyright:
© King Fahd University of Petroleum & Minerals 2024.

Keywords

  • Bug severity classification
  • Bug tracking system
  • Default severity
  • Machine learning

ASJC Scopus subject areas

  • General

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