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A Machine Learning Approach for Classifying the Default Bug Severity Level

  • Research Article-Computer Engineering and Computer Science
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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.

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Notes

  1. https://github.com/arakhia/ML-Default-Bug-Severity-Classification/blob/main/data_queries.sql

  2. https://github.com/arakhia/ML-Default-Bug-Severity-Classification

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Acknowledgements

The authors acknowledge the support of King Fahd University of Petroleum and Minerals in the development of this work.

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Authors and Affiliations

  1. Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    Abdalrahman Aburakhia & Mohammad Alshayeb

  2. Interdisciplinary Research Center for Intelligent Secure Systems, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    Mohammad Alshayeb

Authors
  1. Abdalrahman Aburakhia
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  2. Mohammad Alshayeb
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Corresponding author

Correspondence to Mohammad Alshayeb.

Appendix

Appendix

See Figs. 5, 6, 7, 8 and 9.

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RichFaces Project (RQ1)

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Hadoop Common Project (RQ1)

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HBase Project (RQ1)

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Grails Project (RQ1)

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Tools (JBoss Tools) Project (RQ1)

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Aburakhia, A., Alshayeb, M. A Machine Learning Approach for Classifying the Default Bug Severity Level. Arab J Sci Eng (2024). https://doi.org/10.1007/s13369-024-09081-8

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