When evaluating assurance cases, being able to capture the confidence one has in the individual evidence nodes is crucial, as these values form the foundation for determining the confidence one has in the assurance case as a whole. Human opinions are subjective, oftentimes with uncertainty—it is difficult to capture an opinion with a single probability value. Thus, we believe that a distribution best captures a human opinion such as confidence. Previous work used a doubly-truncated normal distribution or a Dempster-Shafer theory-based belief mass to represent confidence in the evidence nodes, but we argue that a beta distribution is more appropriate. The beta distribution models a variety of shapes and we believe it provides an intuitive way to represent confidence. Furthermore, there exists a duality between the beta distribution and subjective logic, which can be exploited to simplify mathematical calculations. This paper is the first to apply this duality to assurance cases.
Duan, L., Rayadurgam, S., Heimdahl, M. P. E., Sokolsky, O., & Lee, I. (2015). Representing confidence in assurance case evidence. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9338, pp. 15–26). Springer Verlag. https://doi.org/10.1007/978-3-319-24249-1_2