%0 Journal Article
%T Statistical and Machine Learning Methods for Software Fault Prediction Using CK Metric Suite: A Comparative Analysis
%A Yeresime Suresh
%A Lov Kumar
%A Santanu Ku. Rath
%J ISRN Software Engineering
%D 2014
%R 10.1155/2014/251083
%X 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. 1. Introduction Present day software development is mostly based on object-oriented paradigm. The quality of object-oriented software can be best assessed by the use of software metrics. A number of metrics have been proposed by researchers and practitioners to evaluate the quality of software. These metrics help to verify the quality attributes of a software such as effort and fault proneness. The usefulness of these metrics lies in their ability to predict the reliability of the developed software. In practice, software quality mainly refers to reliability, maintainability, and understandability. Reliability is generally measured by the number of faults found in the developed software. Software fault prediction is a challenging task for researchers before the software is released. Hence, accurate fault prediction is one of the major goals so as to release a software having the least possible faults. This paper aims to assess the influence of CK metrics, keeping in view of predicting faults for an open-source software product. Statistical methods such as linear regression and logistic regression are used for classification of faulty classes. Machine learning algorithms such as artificial neural network (ANN), functional link artificial neural network (FLANN), and radial basis function network (RBFN) are applied for prediction of faults, and probabilistic neural
%U http://www.hindawi.com/journals/isrn.software.engineering/2014/251083/