Departmental Papers (CIS)

Date of this Version


Document Type

Conference Paper


IEEE High Assurance Systems Engineering Symposium (HASE 2016), Orlando, Florida, USA, January 7 - 9, 2016.


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 the uncertainty 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.

Subject Area

CPS Theory

Publication Source

2016 IEEE 17th International Symposium on High Assurance Systems Engineering (HASE)

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Copyright/Permission Statement

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Bib Tex

author={L. Duan and S. Rayadurgam and M. Heimdahl and O. Sokolsky and I. Lee},
booktitle={2016 IEEE 17th International Symposium on High Assurance Systems Engineering (HASE)},
title={Representation of Confidence in Assurance Cases Using the Beta Distribution},
keywords={safety-critical software;statistical distributions;uncertainty handling;Baconian probability ratio;assurance case;beta distribution;confidence quantification;confidence representation;continuous probability distribution;discrete ratio;reasoning structure;uncertainty quantification;uncertainty visualization;Fault diagnosis;Safety;Shape;Software;Testing;Uncertainty;Visualization},



Date Posted: 06 April 2016

This document has been peer reviewed.