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Assessing (Software) Reliability Growth Using a Random Coefficient Autoregressive Process and Its Ramifications
December 1985 (vol. 11 no. 12)
pp. 1456-1464
N.D. Singpurwalla, Institute for Reliability and Risk Analysis, School of Engineering and Applied Science, George Washington University
In this paper we motivate a random coefficient autoregressive process of order 1 for describing reliability growth or decay. We introduce several ramifications of this process, some of which reduce it to a Kalman Filter model. We illustrate the usefulness of our approach by applying these processes to some real life data on software failures. Finally, we make a pairwise comparison of the models in terms of the ratio of likelihoods of their predictive distributions, and identify the "best" model.
Index Terms:
software reliability, Dynamic linear and nonlinear models, Kalman Filtering, likelihood ratios, predictive distributions, prequential analysis, random coefficient autoregressive processes, reliability growth
Citation:
N.D. Singpurwalla, R. Soyer, "Assessing (Software) Reliability Growth Using a Random Coefficient Autoregressive Process and Its Ramifications," IEEE Transactions on Software Engineering, vol. 11, no. 12, pp. 1456-1464, Dec. 1985, doi:10.1109/TSE.1985.231889
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