Software Fault Prediction Using LSSVM with Different Kernel Functions

Abstract

Software fault prediction is a process, which helps to identify fault prone modules in early stages of software development. It also helps in improving the software quality with optimized effort and cost. Least Square Support Vector Machines (LSSVM) have been explored in problems related to classification. The aim of this paper is to develop and compare, software fault prediction models using LSSVM with Linear, Polynomial and Radial Basis Function (RBF) kernels. The proposed models classify a software module as faulty or non faulty by taking software metrics such as Halstead software metrics as input. Experiments on fifteen open source projects are performed to study the impact of the proposed models. The models are evaluated using Accuracy, F-measure and ROC AUC as the performance measures. The experimental results shows that, LSSVM with polynomial kernel perform better than LSSVM with linear kernel and similar to RBF kernel, and the models developed using LSSVM improve the prediction accuracy of software fault prediction, compared to the most frequently used models.