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In this paper, comparisons of allocation policies of components in two-parallel-series systems with two types of components are provided with respect to both hazard rate and reversed hazard rate orders. The main results indicate that the lifetime of these kinds of system is stochastically maximized by unbalancing the two classes of components as much as possible. We only assume that the two distributions implied in the model have proportional hazard rates. The same type of comparisons are also given for the dual model, the two-series-parallel systems, but assuming that the distributions implied in the model have proportional reversed hazard rates, and therefore the final conclusion is the opposite; that is, the reliability of the system improves as the similarity between the two parallel subsystems increases. [ABSTRACT FROM PUBLISHER]

In this paper, we describe statistical inference and prediction for software reliability models in the presence of covariate information. Specifically, we develop a semiparametric, Bayesian model using Gaussian processes to estimate the numbers of software failures over various time periods when it is assumed that the software is changed after each time period and that software metrics information is available after each update. Model comparison is also carried out using the deviance information criterion, and predictive inferences on future failures are shown. Real-life examples are presented to illustrate the approach. [ABSTRACT FROM AUTHOR]