Abstract
Classifying a bug and assigning it to skilled and proficient developer is a critical task of bug triaging process. Identifying the type of bug and its severity is among the most vital features of the reported bug report that is required to be fixed rapidly. Manually classifying and identifying the bugs based on the feature is time consuming and tedious process. To automate this task, we propose bug classifying and assigning system-BCAS, which aims to improve the accuracy and assigning process and will provide support to the person reporting the bug assigned to him. To gain the best accuracy, this approach also considers and compares three well-known classification algorithms namely support vector machine, logistic, and Naïve Bayes for classifying bug reports on the basis of severity and component. The proposed work is trained and tested using three open-source software bug dataset from standard Bugzilla repository. Among these classification algorithms, SVM provides best accuracy of 91.6, 80.5, and 81.6% for Eclipse, Firefox, and Mozilla Core projects.
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Mahajan, G., Chaudhary, N. (2022). Bug Classifying and Assigning System (BCAS): An Automated Framework to Classify and Assign Bugs. In: Somani, A.K., Mundra, A., Doss, R., Bhattacharya, S. (eds) Smart Systems: Innovations in Computing. Smart Innovation, Systems and Technologies, vol 235. Springer, Singapore. https://doi.org/10.1007/978-981-16-2877-1_49
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DOI: https://doi.org/10.1007/978-981-16-2877-1_49
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