Representation of Confidence in Assurance Cases Using the Beta Distribution

Lian Duan, Sanjai Rayadurgam, Mats Heimdahl, Oleg Sokolsky, Insup Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

Assurance cases are used to document an argument that a system - such as a critical software system - satisfies some desirable property (e.g., safety, security, or reliability). Demonstrating high confidence that the claims made based on an assurance case can be trusted is crucial to the success of the case. Researchers have proposed quantification of confidence as a Baconian probability ratio of eliminated concerns about the assurance case to the total number of identified concerns. In this paper, we extend their work by mapping this discrete ratio to a continuous probability distribution - a beta distribution - enabling different visualizations of the confidence in a claim. Further, the beta distribution allows us to quantify and visualize theuncertainty associated with the expressed confidence. Additionally, by transforming the assurance case into a reasoning structure, we show how confidence calculations can be performed using beta distributions.

Original languageEnglish (US)
Title of host publicationProceedings - 17th IEEE International Symposium on High Assurance Systems Engineering, HASE 2016
EditorsRadu Babiceanu, Helene Waeselynck, Jie Xu, Raymond A. Paul, Bojan Cukic
PublisherIEEE Computer Society
Pages86-93
Number of pages8
ISBN (Electronic)9781467399128
DOIs
StatePublished - Mar 1 2016
Event17th IEEE International Symposium on High Assurance Systems Engineering, HASE 2016 - Orlando, United States
Duration: Jan 7 2016Jan 9 2016

Publication series

NameProceedings of IEEE International Symposium on High Assurance Systems Engineering
Volume2016-March
ISSN (Print)1530-2059

Other

Other17th IEEE International Symposium on High Assurance Systems Engineering, HASE 2016
CountryUnited States
CityOrlando
Period1/7/161/9/16

Bibliographical note

Funding Information:
Acknowledgment: This work has been partially supported by NSF grant CNS- 1035715.

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