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Developing classifiers by considering sentiment analysis of reported bugs for priority prediction

Author

Listed:
  • Anisha Singh

    (Jawaharlal Nehru University)

  • P. K. Kapur

    (Amity University)

  • V. B. Singh

    (Jawaharlal Nehru University)

Abstract

Software systems behave abnormally due to bugs when it comes into the operational phase. Lack of proper understanding of customer requirements, implementation, knowledge, wrong algorithmic designing, and other issue is also the reason for bug production. To fix those flaws, developers request to the users for feedback. Users have had issues with the software systems that have been released. Users are encouraged to submit their issues to issue-tracking systems such as Bugzilla, Mantis, Google Code Issue Tracker, GitHub Issue Tracker, and Jira to improve the next version of the product and meet user needs. Manual prioritization is time-consuming and inconvenient. In this research paper, we propose using sentiment analysis to anticipate the report's priority. This is the first time the sentiment-based approach used for a bug report to prioritize prediction on open-source projects. First, we take the bug report summary and use natural language pre-processing techniques to clean the text and pre-process the bug report. Second, sentiment analysis is applied to clean texts that contain sentiments of terms. Third, we use TF-IDF to construct a feature vector for bug reports, fourth, we used resampling techniques to balance the dataset, and then we used different machine learning classifiers to train historical data namely Bugzilla open-source projects to forecast their priority. The proposed method we have used improves the performance of the classifier with sentiment comparison to without sentiment on average f-score 2–10%.

Suggested Citation

  • Anisha Singh & P. K. Kapur & V. B. Singh, 2024. "Developing classifiers by considering sentiment analysis of reported bugs for priority prediction," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(5), pages 1888-1899, May.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:5:d:10.1007_s13198-023-02199-2
    DOI: 10.1007/s13198-023-02199-2
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    References listed on IDEAS

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    1. Alim Al Ayub Ahmed & Sugandha Agarwal & IMade Gede Ariestova Kurniawan & Samuel P. D. Anantadjaya & Chitra Krishnan, 2022. "Business boosting through sentiment analysis using Artificial Intelligence approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 699-709, March.
    2. V. B. Singh & Sanjay Misra & Meera Sharma, 2017. "Bug Severity Assessment in Cross Project Context and Identifying Training Candidates," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 16(01), pages 1-30, March.
    3. Muhammad Zubair Asghar & Aurangzeb Khan & Shakeel Ahmad & Imran Ali Khan & Fazal Masud Kundi, 2015. "A Unified Framework for Creating Domain Dependent Polarity Lexicons from User Generated Reviews," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-19, October.
    Full references (including those not matched with items on IDEAS)

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