A. Mazak, M. Wimmer, P. Patsuk-Bösch: Execution-based Model Profiling, in Post-Proceedings of the Post-Proceeding of the 6th International Symposium on Data-Driven Process Discovery, LNCS; herausgegeben von: Springer, 2017, ISBN: 978-3-319-74160-4, pages 1 - 20. doi: 10.1007/978-3-319-74161-1_3

In model-driven engineering (MDE), models are mostly used in prescriptive ways for system engineering. While prescriptive models are indeed an important ingredient to realize a system, for later phases in the systems’ lifecycles additional model types are beneficial to use. Unfortunately, current MDE approaches mostly neglect the information upstream in terms of descriptive models from operations to (re)design phases. To tackle this limitation, we propose execution-based model profiling as a continuous process to improve prescriptive models at design-time through runtime information. This approach incorporates knowledge in terms of model profiles from execution logs of the running system. To accomplish this, we combine techniques of process mining with runtime models of MDE. In the course of a case study, we make use of a traffic light system example to demonstrate the feasibility and benefits of the introduced execution-based model profiling approach.

Execution-based Model Profiling