A statistical comparison for evaluating the effectiveness of linear and nonlinear manifold detection techniques for software defect prediction

Abstract

Most of the software systems are released without predicting defects and therefore, this paper presents a new effective technique - manifold detection technique (MDT) is essential and different than earlier applied defect prediction methods like regression, feature selection methods, etc. In this paper, performance of classifiers has been compared with or without MDTs to evaluate the effectiveness of different MDTs (linear and nonlinear) by reducing the dimensions of software datasets. In this process, eight classifiers were applied to four PROMISE datasets to determine the best performing classifier with respect to prediction performance measuring factors (accuracy, precision, recall, F-measure, AUC, misclassification error) with or without MDTs. The experimental results statistically tested by paired two-tailed t-test proved that FastMVU is the most accurate result producing technique as compared to all other nonlinear MDTs and Bayesian network (BN) is the most effective technique for software defect prediction using with or without MDTs.