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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.