Software Reliability Prediction with Ensemble Method and Virtual Data Point Incorporation

Abstract

Software reliability is one of the key aspects of software quality estimation and prediction during software testing period. Hence, accurate prediction of software reliability is an important but critical job. Machine Learning (ML) techniques have been proven victorious in providing superior results than traditional techniques for software reliability prediction. Generally, ML models entail sufficient data for training to achieve improved generalization. Inadequate training data may lead to land at suboptimal solution. This article suggests a method of enriching training dataset through exploration and incorporation of virtual data from existing data. For boost up the overall accuracy and reducing the risk of model selection an ensemble framework of five ML methods is suggested. The extended software reliability dataset is then exposed to the constituent models as well as the ensemble approach separately to estimate the future data. Extensive simulation results on a couple of software reliability datasets reveal that our proposed model significantly improves the prediction accuracy.