Improving Software Reliability Predictions Through Incorporating Learning Effects

  • Lutfiah Ismail Al turk Statistics Department, Faculty of Sciences, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia
Keywords: Non-homogeneous Poisson process, log-logistic distribution, learning effects, goodness-of-fit performance, non-linear least squares estimation.


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.


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