Statistical and Machine Learning Methods for Software Fault Prediction Using CK Metric Suite: A Comparative Analysis

Abstract

Experimental validation of software metrics in fault prediction for object-oriented methods using statistical and machine learning methods is necessary. By the process of validation the quality of software product in a software organization is ensured. Object-oriented metrics play a crucial role in predicting faults. This paper examines the application of linear regression, logistic regression, and artificial neural network methods for software fault prediction using Chidamber and Kemerer (CK) metrics. Here, fault is considered as dependent variable and CK metric suite as independent variables. Statistical methods such as linear regression, logistic regression, and machine learning methods such as neural network (and its different forms) are being applied for detecting faults associated with the classes. The comparison approach was applied for a case study, that is, Apache integration framework (AIF) version 1.6. The analysis highlights the significance of weighted method per class (WMC) metric for fault classification, and also the analysis shows that the hybrid approach of radial basis function network obtained better fault prediction rate when compared with other three neural network models.