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In the literature on statistical inference in software reliability, the assumptions of parametric models and random sampling of bugs have been pervasive. We argue that both assumptions are problematic, the first because of robustness concerns and the second due to logical and practical difficulties. These considerations motivate the approach taken in this paper. We propose a nonparametric software reliability model based on the order-statistic paradigm. The objective of the work is to estimate, from data on discovery times observed within a type I censoring framework, both the underlying distribution F from which discovery times are generated and N, the unknown number of bugs in the software. The estimates are used to predict the next time to failure. The approach makes use of Bayesian nonparametric inference methods, in particular, the beta-Stacy process. The proposed methodology is illustrated on both real and simulated data.
Beta-Stacy process, order statistics, reliability, testing strategies, nonparametric statistics, survival analysis.

F. J. Samaniego and S. P. Wilson, "Nonparametric Analysis of the Order-Statistic Model in Software Reliability," in IEEE Transactions on Software Engineering, vol. 33, no. , pp. 198-208, 2007.
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