Effect of Principle Component Analysis and Support Vector Machine in Software Fault Prediction

Machine Learning (ML) approaches have a great impact in fault prediction. Demand for producing quality assured software in an organization has been rapidly increased during the last few years. This leads to increase in development of machine learning algorithms for analyzing and classifying the data sets, which can be used in constructing models for predicting the important quality attributes such as fault proneness. Defective modules in software project have a considerable risk which reduces the quality of the software. This paper mainly addresses the software fault prediction using hybrid Support Vector Machine (SVM) classifier. We conduct a comparative study using the WEKA tool for three different levels of software metrics (package level, class level and method level) with hybrid SVM classifiers using feature selection techniques such as Principle Component Analysis (PCA). The experiments are carried out on the datasets such as NASA KC1 method level data set, NASA KC1 class level dataset and Eclipse dataset for package level metrics. The feature selection techniques evolved by experiments shows that Principle Component Analysis (PCA) with hybrid SVM performs better than other feature selection techniques.