Developing a hybrid software reliability growth model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Avinash Kumar Shrivastava ◽  
Ruchi Sharma

PurposeThe purpose of this paper is to develop a new software reliability growth model considering different fault distribution function before and after the change point.Design/methodology/approachIn this paper, the authors have developed a framework to incorporate change-point in developing a hybrid software reliability growth model by considering different distribution functions before and after the change point.FindingsNumerical illustration suggests that the proposed model gives better results in comparison to the existing models.Originality/valueThe existing literature on change point-based software reliability growth model assumes that the fault correction trend before and after the change is governed by the same distribution. This seems impractical as after the change in the testing environment, the trend of fault detection or correction may not follow the same trend; hence, the assumption of same distribution function may fail to predict the potential number of faults. The modelling framework assumes different distributions before and after change point in developing a software reliability growth model.

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tahere Yaghoobi

PurposeThe Gompertz curve has been used in industry to estimate the number of remaining software faults. This paper aims to introduce a family of distributions for fitting software failure times which subsumes the Gompertz distribution.Design/methodology/approachThe mean value function of the corresponding non-homogenous Poisson process software reliability growth model is presented. Model parameters are estimated by the method of maximum likelihood. A comparison of the new model with eight models that use well-known failure time distributions of exponential, gamma, Rayleigh, Weibull, Gompertz, half normal, log-logistic and lognormal is performed according to the several statistical and informational criteria. Moreover, a Shannon entropy approach is used for ranking and model selection.FindingsNumerical experiments are implemented on five real software failure datasets varying from small to large datasets. The results exhibit that the proposed model is promising and particularly outperforms the Gompertz model in all considered datasets.Originality/valueThe proposed model provides optimized reliability estimation.


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