A greedy reliability estimator for usage-based statistical testing

Lan Lin, Jesse H. Poore, Stacy J. Prowell

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Markov chain usage models have been a basis for statistical testing of software intensive systems for more than two decades. During this time, several reliability estimators have been formulated and used in testing. This paper presents an improvement on the arc-based Bayesian estimator distributed with Version 4.5 of the JUMBL (J Usage Model Builder Library) [1]. The arc-based Bayesian estimator is conservative, and especially so for samples that are small relative to the entropy in the model. We call the new model the 'greedy estimator' because it combines the specific information from testing with the inference attributed to the total population. The greedy estimator is shown analytically and experimentally to give more accurate estimates than its predecessor on concrete models, although they converge in the long run. The results of using the greedy estimator are demonstrated for a set of testing data for a tape drive controller.

Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS
EditorsM. Surendra Prasad Babu, Li Wenzheng, Eric Tsui
PublisherIEEE Computer Society
Pages86-89
Number of pages4
ISBN (Electronic)9781479932788
DOIs
StatePublished - Oct 21 2014
Event2014 5th IEEE International Conference on Software Engineering and Service Science, ICSESS 2014 - Beijing, China
Duration: Jun 27 2014Jun 29 2014

Publication series

NameProceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS
ISSN (Print)2327-0586
ISSN (Electronic)2327-0594

Conference

Conference2014 5th IEEE International Conference on Software Engineering and Service Science, ICSESS 2014
Country/TerritoryChina
CityBeijing
Period06/27/1406/29/14

Keywords

  • Markov chain usage models
  • reliability estimation
  • statistical testing
  • system reliability

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