Abstract
We investigate prior sensitivity to predictions of software reliability made with two well-known software reliability models; one based on a nonhomogeneous Poisson process and the other a time series. A mixture of formal (global) and informal sensitivity approaches is used. We demonstrate that while inference based on the first of these models does not seem too sensitive to the prior input, inference from the time series model does exhibit considerable prior sensitivity, even when the sample of observed data is quite large.
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Wilson, S.P., Wiper, M.P. (2000). Prior Robustness in Some Common Types of Software Reliability Model. In: Insua, D.R., Ruggeri, F. (eds) Robust Bayesian Analysis. Lecture Notes in Statistics, vol 152. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1306-2_21
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DOI: https://doi.org/10.1007/978-1-4612-1306-2_21
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