Machine Learning-Based Methods for Identifying Bug Severity Level from Bug Reports-Reference-Cited by-同舟云学术

Machine Learning-Based Methods for Identifying Bug Severity Level from Bug Reports

Published:2023 Issue: Volume: Page:199-208
ISSN:2367-3370
Container-title:Proceedings of the 19th International Conference on Computing and Information Technology (IC2IT 2023)
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Author:

Sarawan Kamthon,Polpinij Jantima,Luaphol Bancha

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-30474-3_17

Reference18 articles.

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2. Tian, Y., Lo, D., Sun, C.: DRONE: predicting priority of reported bugs by multi-factor analysis. In: 2013 29th IEEE International Conference on Software Maintenance (ICSM), pp. 200–209, Eindhoven, Netherlands (2013)

3. Ahsan, S.N., Ferzund, J., Wotawa, F.: Automatic software bug triage system (BTS) based on latent semantic indexing and support vector machine. In: ICSEA 2009. Fourth International Conference on Software Engineering Advances, pp. 216–221, Porto, Portugal (2009)

4. Lamkanfi, A., Demeyer, S., Giger, E., Goethals, B.: Predicting the severity of a reported bug. In: 2010 7th IEEE Working Conference on Mining Software Repositories (MSR), pp.1–10, Cape Town, South Africa (2010)

5. Defect severity classification in software testing (with an example). http://www.zyxware.com/articles/3559/defect-severity-classification-insoftware-testing-with-an-example. Accessed 24 May 2022

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