Prioritization of Software Bugs Using Entropy-Based Measures

Abstract

Open-source software is evolved through the active participation of users. In general, a user request for bug fixing, the addition of new features, and feature enhancements. Due to this, the software repositories are increasing day by day at an enormous rate. Additionally, user distinct requests add uncertainty and irregularity to the reported bug data. The performance of machine learning algorithms drastically gets influenced by the inappropriate handling of uncertainty and irregularity in the bug data. Researchers have used machine learning techniques for assigning priority to the bug without considering the uncertainty and irregularity in reported bug data. In order to capture the uncertainty and irregularity in the reported bug data, the summary entropy–based measure in combination with the severity and summary weight is considered in this study to predict the priority of bugs in the open-source projects. Accordingly, the classifiers are build using these measures for different machine learning techniques, namely, k-nearest neighbor (KNN), naïve Bayes (NB), J48, random forest (RF), condensed nearest neighbor (CNN), multinomial logistic regression (MLR), decision tree (DT), deep learning (DL), and neural network (NNet) for bug priority prediction This research aims to systematically analyze the summary entropy–based machine learning classifiers from three aspects: type of machine learning technique considered, estimation of various performance measures: Accuracy, Precision, Recall, and F-measure and through existing model comparison. The experimental analysis is carried out using three open-source projects, namely, Eclipse, Mozilla, and OpenOffice. Out of 145 cases (29 products X 5 priority levels), the J48, RF, DT, CNN, NNet, DL, MLR, and KNN techniques give the maximum F-measure for 46, 35, 28, 11, 15, 4, 3, and 1 cases, respectively. The result shows that the proposed summary entropy–based approach using different machine learning techniques performs better than without entropy-based approach and also entropy-based approach improves the Accuracy and F-measure as compared with the existing approaches. It can be concluded that the classifier build using summary entropy measure significantly improves the machine learning algorithms' performance with appropriate handling of uncertainty and irregularity. Moreover, the proposed summary entropy–based classifiers outperform the existing models available in the literature for predicting bug priority.