Application of genetic algorithm as feature selection technique in development of effective fault prediction model

Prediction of faults in a proposed software is helpful in deciding the amount of effort to be given for software development. We observed that, a good number of authors hypothesized that the performance of fault prediction model depends on the source code metrics which are used as input of the model. Feature selection technique is a process of selecting suitable set of source code metrics which may improve the performance of fault prediction model. In this work, genetic algorithm (GA) has been applied as feature selection technique to select the suitable set of source code metrics. This selected set of source code metrics are used as requisite input data to develop a classifier using five different classification techniques such as logistic regression, extreme learning machine, support vector machine (SVM) with three different kernel functions (linear, polynomial, and radial basis kernel functions) in order to predict the faulty and non-faulty classes. In this study, we propose a cost evaluation framework to perform cost based analysis for evaluating the effectiveness of fault prediction model. We perform experiments on thirty number of Java Open Source projects. From the obtained results, it is observed that the model developed using selected set of source code metrics obtained better result as compared to all metrics. From costs analysis framework, it is observed that the developed fault prediction model is best suitable for software with % of faulty classes less than the threshold value depending on fault identification efficiency (low-46.44%, median-45.37%, and high-36.63%).