Abstract
31Aug 2016 Predicting Bug severity using Classification on Clustered Bugs Data. Rajalakshmi R and Dhanya P M. Department of Computer Science and Engineering, Rajagiri School of Engineering and Technology Kochi, India.
Highlights
Algorithms in data mining helps to predict the severity of the bug
The system studies the effect of clustering before classification
Classification was applied to the clusters obtained, to assign severity labels to bugs based on priority, product and component attributes
Summary
Algorithms in data mining helps to predict the severity of the bug. Classification was applied to the clusters obtained, to assign severity labels to bugs based on priority, product and component attributes. A comparative study of different combinations of classification and clustering algorithm on the performance of prediction is undertaken in this work. Prediction of developer with correct severity helps in bug management process. Text mining or knowledge discovery from text (KDT) deals with the machine supported analysis of text It uses techniques from information retrieval, information extraction as well as natural language processing (NLP) and connects them with the algorithms and methods of Knowledge Discovery in Data (KDD), data mining, machine learning and statistics. Pattern proximity is usually measured by a distance. Experiments focus on general framework of classifier combination when one learner is unsupervised
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