Assessing (Software) Reliability Growth Using a Random Coefficient Autoregressive Process and Its Ramifications
Issue No. 12 - December (1985 vol. 11)
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.
software reliability, Dynamic linear and nonlinear models, Kalman Filtering, likelihood ratios, predictive distributions, prequential analysis, random coefficient autoregressive processes, reliability growth
N. Singpurwalla and R. Soyer, "Assessing (Software) Reliability Growth Using a Random Coefficient Autoregressive Process and Its Ramifications," in IEEE Transactions on Software Engineering, vol. 11, no. , pp. 1456-1464, 1985.