Modeling on Generalized Extended Inverse Weibull Software Reliability Growth Model

Journal of Data Science

article

David D. Hanagal¹  Nileema N. Bhalerao¹ 

[1] Department of Statistics, Savitribai Phule Pune University

关键词: Akaikes information criterion;    Generalized extended inverse Weibull distribution;    Hazard function;    Predictive ratio risk;   

DOI  :  10.6339/JDS.201907_17(3).0007

学科分类：土木及结构工程学

来源: JDS

【 摘 要 】

In this paper we introduce the generalized extended inverse Weibull finite failure software reliability growth model which includes both increasing/decreasing nature of the hazard function. The increasing/decreasing behavior of failure occurrence rate fault is taken into account by the hazard of the generalized extended inverse Weibull distribution. We proposed a finite failure non-homogeneous Poisson process (NHPP) software reliability growth model and obtain unknown model parameters using the maximum likelihood method for interval domain data. Illustrations have been given to estimate the parameters using standard data sets taken from actual software projects. A goodness of fit test is performed to check statistically whether the fitted model provides a good fit with the observed data. We discuss the goodness of fit test based on the Kolmogorov-Smirnov (K-S) test statistic. The proposed model is compared with some of the standard existing models through error sum of squares, mean sum of squares, predictive ratio risk and Akaikes information criteria using three different data sets. We show that the observed data fits the proposed software reliability growth model. We also show that the proposed model performs satisfactory better than the existing finite failure category models.

【 授权许可】

CC BY