In prior work, we introduced a framework called Assurance 2.0, which aims to enhance the rigor of assurance cases by emphasizing the reasoning process, evidence utilized, and explicit identification of counter-claims (defeaters) and counter-evidence. In this paper, we present a new approach to enhancing Assurance 2.0 by incorporating semantic rule-based analysis capabilities. Firstly, we systematically convert the assurance case into Prolog predicates and constraints. Then, leveraging the analysis capabilities of the s(CASP), a goal-directed top-down solver for Constraints Answer Set Programs, we evaluate the semantic properties of assurance cases, including logical consistency, completeness, and indefeasibility. The application of these analyses provides both authors and evaluators with higher confidence when assessing the assurance case.
@inproceedings{Murugesan-etal:GDE23, booktitle={International Conference on Logic Programming 2023 Workshops: Goal-Directed Execution of Answer Set Programs (GDE)}, title = {Semantic Analysis of Assurance Cases Using {s(CASP)}}, author = {Anitha Murugesan and others}, month = jul, address = {London, UK}, year={2023}, volume={3437}, series={CEUR Workshop Proceedings}, publisher = {} } alternative full author list: author = {Anitha Murugesan and Isaac Hong Wong and Robert Stroud and Joaquín Arias and Elmer Salazar and Gopal Gupta and Robin Bloomfield and Srivatsan Varadarajan and John Rushby},