Assurance cases are structured logical arguments supported by evidence that explain how systems, possibly software systems, satisfy desirable properties for safety, security or reliability. The confidence in both the logical reasoning and the underlying evidence is a factor that must be considered carefully when evaluating an assurance case; the developers must have confidence in their case before the system is delivered and the assurance case reviewer, such as a regulatory body, must have adequate confidence in the case before approving the system for use. A necessary aspect of gaining confidence in the assurance case is dealing with uncertainty, which may have several sources. Uncertainty, often impossible to eliminate, nevertheless undermines confidence and must therefore be sufficiently bounded. It can be broadly classified into two types, aleatory (statistical) and epistemic (systematic). This paper surveys how researchers have reasoned about uncertainty in assurance cases. We analyze existing literature to identify the type of uncertainty addressed and distinguish between qualitative and quantitative approaches for dealing with uncertainty.
|Original language||English (US)|
|Title of host publication||Software Engineering in Health Care - 4th International Symposium, FHIES 2014 and 6th International Workshop, SEHC 2014, Revised Selected Papers|
|Editors||Michaela Huhn, Laurie Williams|
|Number of pages||17|
|State||Published - 2017|
|Event||4th International Symposium on Foundations of Health Information Engineering and Systems, FHIES 2014 and 6th International Workshop on Software Engineering in Health Care, SEHC 2014 - Washington, United States|
Duration: Jul 17 2014 → Jul 18 2014
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||4th International Symposium on Foundations of Health Information Engineering and Systems, FHIES 2014 and 6th International Workshop on Software Engineering in Health Care, SEHC 2014|
|Period||7/17/14 → 7/18/14|
Bibliographical noteFunding Information:
This work has been partially supported by NSF grants CNS-0931931 and CNS-1035715 A. Ayoub—Currently employed at Mathworks.
© Springer International Publishing AG 2017.