A Machine Learning Approach for Classifying the Default Bug Severity Level
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s13369-024-09081-8.pdf
Reference41 articles.
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2. Lamkanfi, A., Demeyer, S., Soetens, Q.D., Verdonck, T.: Comparing mining algorithms for predicting the severity of a reported bug. In: 2011 15th European Conference on Software Maintenance and Reengineering, 2011, pp. 249–258 (2011), https://doi.org/10.1109/CSMR.2011.31
3. 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 2010), 2010, pp 1–10 (2010), https://doi.org/10.1109/MSR.2010.5463284
4. Tian, Y.; Ali, N.; Lo, D.; Hassan, A.E.: On the unreliability of bug severity data. Empir. Softw. Eng. 21, 2298–2323 (2015). https://doi.org/10.1007/s10664-015-9409-1
5. Gomes, L.A.F.; da Silva Torres, R.; Côrtes, M.L.: Bug report severity level prediction in open source software: a survey and research opportunities. Inf. Softw. Technol. 115, 58–78 (2019). https://doi.org/10.1016/j.infsof.2019.07.009
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