Assessing (Software) Reliability Growth Using a Random Coefficient Autoregressive Process and Its Ramifications

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Nozer D. Singpurwalla
  • Refik Soyer

Detail(s)

Original languageEnglish
Pages (from-to)1456-1464
Journal / PublicationIEEE Transactions on Software Engineering
VolumeSE-11
Issue number12
Publication statusPublished - Dec 1985
Externally publishedYes

Abstract

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. Copyright © 1985 by The Institute of Electrical and Electronics Engineers, Inc.

Research Area(s)

  • Dynamic linear and nonlinear models, Kalman Filtering, likelihood ratios, predictive distributions, prequential analysis, random coefficient autoregressive processes, reliability growth, software reliability