A software reliability growth model with Gompertz-logarithmic failure time distribution
International Journal of Quality & Reliability Management
ISSN: 0265-671X
Article publication date: 9 December 2020
Issue publication date: 16 July 2021
Abstract
Purpose
The 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/approach
The 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.
Findings
Numerical 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/value
The proposed model provides optimized reliability estimation.
Keywords
Acknowledgements
There is no potential conflict or interest in the research.
Citation
Yaghoobi, T. (2021), "A software reliability growth model with Gompertz-logarithmic failure time distribution", International Journal of Quality & Reliability Management, Vol. 38 No. 7, pp. 1576-1592. https://doi.org/10.1108/IJQRM-04-2020-0098
Publisher
:Emerald Publishing Limited
Copyright © 2020, Emerald Publishing Limited