Improving Software Reliability Predictions Through Incorporating Learning Effects
Lutfiah Ismail Al turk · American Scientific Research Journal for Engineering, Technology, and Sciences · 2019
Software reliability is one of the major metrics for software quality evaluation. In reliability engineering, testing phase specifying the process of measuring software reliability. In this paper; we examine the effect of incorporating the autonomous errors detected factor and learning factor in enhancing the prediction accuracy with application to the software failure data. For this purpose, Non-Homogenous Poisson Process (NHPP) model with the perspective of learning effects based on the Log-Logistic (LL) distribution is proposed. The parameter estimation using the Non-Linear Least Squares Estimation (NLSE) method is conducted. Two goodness-of-fit tests are used to evaluate the proposed models. This paper encourages software developers for considering the learning effects property in software reliability modeling.