Abstract
There can be noise and uncertainty in the bug reports data as the bugs are reported by a heterogeneous group of users working across different countries. Bug description is an essential attribute that helps to predict other bug attributes, such as severity, priority, and time fixes. We need to consider the noise and confusion present in the text of the bug report, as it can impact the output of different machine learning techniques. Shannon entropy has been used in this paper to calculate summary uncertainty about the bug. Bug severity attribute tells about the type of impact the bug has on the functionality of the software. Correct bug severity estimation allows scheduling and repair bugs and hence help in resource and effort utilization. To predict the severity of the bug we need software project historical data to train the classifier. These training data are not always available in particular for new software projects. The solution which is called cross project prediction is to use the training data from other projects. Using bug priority, summary weight and summary entropy, we have proposed cross project bug severity assessment models. Results for proposed summary entropy based approach for bug severity prediction in cross project context show improved performance of the Accuracy and F-measure up to 70.23% and 93.72% respectively across all the machine learning techniques over existing work.
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References
Singh, V.B., Chaturvedi, K.K.: Bug tracking and reliability assessment system. Int. J. Softw. Eng. Appl. 5(4), 17–30 (2011)
Sharma, M., Bedi, P., Chaturvedi, K.K., Singh, V.B.: Predicting the priority of a reported bug using machine learning techniques and cross project validation. In: International Conference Intelligent Systems Design and Applications (ISDA), pp. 27–29. IEEE (2012)
Kumari, M., Sharma, M., Singh, V.B.: Severity assessment of a reported bug by considering its uncertainty and irregular state. Int. J. Open Source Softw. Process. (IJOSSP) 9(4), 20–46 (2018)
Sharma, M., Bedi, P., Singh, V.B.: An empirical evaluation of cross project priority prediction. Int. J. Syst. Assurance Eng. Manage. 5(4), 651–663 (2014). https://doi.org/10.1007/s13198-014-0219-4
Sharma, M., Kumari, M., Singh, R.K., Singh, V.B.: Multiattribute based machine learning models for severity prediction in cross project context. In: Murgante, B., et al. (eds.) ICCSA 2014. LNCS, vol. 8583, pp. 227–241. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09156-3_17
Singh, V.B., Misra, S., Sharma, M.: Bug severity assessment in cross project context and identifying training candidates. J. Inform. Knowl. Manage 16(1), 1750005 (2017)
Menzies, T., Marcus, A.: Automated severity assessment of software defect reports, pp. 346–355. Int. Conf. Softw. Maintenance, IEEE (2008)
Lamkanfi, A., Demeyer, S., Giger, E., Goethals, B.: Predicting the severity of a reported bug. In: Mining Software Repositories (MSR), pp. 1–10 (2010)
Lamkanfi, A., Demeyer, S., Soetens, Q.D., Verdonck, T.: Comparing mining algorithms for predicting the severity of a reported bug. In: CSMR, pp. 249–258 (2011)
Chaturvedi, K.K., Singh, V.B.: Determining bug severity using machine learning techniques. In: CSI-IEEE International Conference on Software Engineering (CONSEG), pp. 378–387 (2012)
Tian, Y., Lo, D., Sun, C.: Information retrieval based nearest neighbor classification for fine-grained bug severity prediction. In WCRE, Kingston, ON, Canada, pp. 215–224. IEEE, New York (2012)
Chaturvedi, K.K., Singh, V.B.: An empirical comparison of machine learning techniques in predicting the bug severity of open and close source projects. Int. J. Open Source Softw. Process. 4(2), 32–59 (2013)
Kumari, M., Singh, V.B.: An Improved classifier based on entropy and deep learning for bug priority prediction. In: Abraham, A., Cherukuri, A.K., Melin, P., Gandhi, N. (eds.) ISDA 2018 2018. AISC, vol. 940, pp. 571–580. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-16657-1_53
Singh, V.B., Sharma, M., Pham, H.: Entropy based software reliability analysis of multi-version open source software. IEEE Trans. Softw. Eng. 44(12), 1207–1223 (2017)
Kumari, M., Misra, A., Misra, S., Fernandez Sanz, L., Damasevicius, R., Singh, V.B.: Quantitative quality evaluation of software products by considering summary and comments entropy of a reported bug. Entropy 21(1), 91 (2019)
IEEE88: IEEE Standard Dictionary of Measures to Produce Reliable Software. IEEE Std 982.1-1988, Institute of Electrical and Electronics Engineers (1989)
Shannon, C.E.: A mathematical theory of communication. Bell System Tech. J., 27, 379–423, 623–656 (1948)
Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: YALE: Rapid prototyping for complex data mining Tasks. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-06) (2006). http://www.rapid-i.com
Chaturvedi, K.K., Singh, V.B.: An empirical comparison of machine learning techniques in predicting the bug severity of open and closed source projects. Int. J. Open Source Softw. Process. (IJOSSP) 4(2), 32–59 (2012)
Yang, C.Z., Hou, C.C., Kao, W.C., Chen, X.: An empirical study on improving severity prediction of bug reports using feature selection. In: Software Engineering Conference (APSEC), 19th Asia-Pacific, vol. 1, pp. 240–249. IEEE (2012)
Iliev, M., Karasneh, B., Chaudron, M. R., Essenius, E.: Automated prediction of bug severity based on codifying design knowledge using ontologies. In: Proceedings of the First International Workshop on Realizing AI Synergies in Software Engineering, pp. 7–11. IEEE Press (2012)
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Kumari, M., Singh, U.K., Sharma, M. (2020). Entropy Based Machine Learning Models for Software Bug Severity Assessment in Cross Project Context. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12254. Springer, Cham. https://doi.org/10.1007/978-3-030-58817-5_66
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