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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.

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Citation
Category
Paper in Conference Proceedings or in Workshop Proceedings (Full Paper in Proceedings)
Event Title
The 32nd ACM Symposium on Applied Computing (SAC 2017)
Divisions
Software Architecture
Subjects
Informatik Allgemeines
Software Engineering
Angewandte Informatik
Theoretische Informatik
Event Location
Marrakesh, Morocco
Event Type
Conference
Event Dates
3-6 Apr 2017
Date
April 2017
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