Reduction Techniques for Efficient Behavioral Model Checking in Adaptive Case Management
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.
Top- Czepa, Christoph
- Tran, Huy
- Zdun, Uwe
- Tran, Thanh
- Weiss, Erhard
- Ruhsam, Christoph
Category |
Paper in Conference Proceedings or in Workshop Proceedings (Paper) |
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 |
Page Range |
pp. 719-726 |
Date |
April 2017 |
Export |