Abstract
Many software reliability growth models (SRGMs) have been developed in the past three decades to estimate software reliability measures such as the number of remaining faults and software reliability. The underlying common assumption of many existing models is that the operating environment and the developing environment are the same. This is often not the case in practice because the operating environments are usually unknown due to the uncertainty of environments in the field. In this paper, we develop a generalized software reliability model incorporating the uncertainty of fault-detection rate per unit of time in the operating environments. A logistic fault-detection software reliability model is derived. Examples are included to illustrate the goodness of fit of the proposed model and existing nonhomogeneous Poisson process (NHPP) models based on a set of failure data. Three goodness-of-fit criteria, such as mean square error, predictive power, and predictive ratio risk are used as an example to illustrate model comparisons. The results show that the proposed logistic fault-detection model fit significantly better than other existing NHPP models based on all three goodness-of-fit criteria.
Highlights
Many existing nonhomogeneous Poisson process (NHPP) software reliability models [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28] have been used through the fault intensity rate function and the mean value functions m(t) within a controlled testing environment to estimate reliability metrics such as the number of residual faults, failure rate, and reliability of the software
Software reliability models are applied to system test data with the hope of estimating the failure rate of the software in user environments
For all these three criteria—mean square error (MSE), predictive ratio risk (PRR), and predictive power (PP)—the smaller the value, the better the model fits, relative to other models run on the same data set
Summary
Many existing NHPP software reliability models [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28] have been used through the fault intensity rate function and the mean value functions m(t) within a controlled testing environment to estimate reliability metrics such as the number of residual faults, failure rate, and reliability of the software. Pham et al [26] recently discussed a new logistic software reliability model where the fault-detection rate per unit time follows a three-parameter logistic function. They did not take into consideration of the uncertainty of operating environment. Substituting the three-parameter logistic function b(t) from Eq (4) into Eq (3), we can obtain the expected number of software failures detected by time t subject to the uncertainty of the environments as follows:.
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