Reduction techniques for efficient behavioral model checking in adaptive case management

Abstract

Case models in Adaptive Case Management (ACM) are business process models ranging from unstructured over semi-structured to structured process models. Due to this versatility, both industry and academia show growing interest in this approach. This paper discusses a model checking approach for the behavioral verification of ACM case models. To counteract the high computational demands of model checking techniques, our approach includes state space reduction techniques as a preprocessing step before state-transition system generation. Consequently, the problem size is decreased, which decreases the computational demands needed by the subsequent model checking as well. An evaluation of the approach with a large set of LTL specifications on two real-world case models, which are representative for semi-structured and structured process models and realistic in size, shows an acceptable performance of the proposed approach.