Indian Journal of Science and Technology

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The objective of this paper is to analyze and identify domain specific priority classification of bug reports. Different classification algorithms namely-Linear Discriminant Analysis (LDA), Naive Bayes (NB) to predict the performance measures are used. The performance of classification algorithms are compared using bug report instances of two open source software (OSS) namely NetBeans and Eclipse is downloaded from Bugzilla bug repository. Principal Component Analysis (PCA) for feature selection, Particle Swam Optimization (PSO) for instance selection to reduce the dimension of the bug reports. The results are compared based on four performance measures i.e. Accuracy, Precision, Recall and Processing Time. Feature selection to Instance selection gives better results than Instance selection to Feature selection is the result influenced from experiment performed. This approach shows that linear Discriminant analysis performs better than Naïve Bayes classifier.

Keywords

Bug Tracking System, Bug Trigging, Feature Selection, Instance Selection, Supervised Algorithms Software Testing.
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