Refactoring Large Process Model Repositories

With the increasing adoption of process-aware information systems, large process model repositories have emerged. Typically, the models in such repos-itories are re-aligned to real-world events and demands through adaptation on a day-to-day basis. This bears the risk of introducing model redundan-cies and of unnecessarily increasing model complexity. If no continuous in-vestment is made in keeping process models simple, changes will become more difficult and error-prone over time. Although refactoring techniques are widely used in software engineering to address similar problems, so far, no comparable state-of-the-art has evolved in the business process manage-ment domain. Process designers either have to refactor process models by hand or are simply unable to apply respective techniques at all. This paper proposes a catalogue of process model “smells ” for identifying refactoring opportunities. In addition, it introduces a set of behavior-preserving tech-niques for refactoring large process repositories. The proposed refactorings enable process designers to effectively deal with model complexity by making process models better understandable and easier to maintain. The refactor-ings have been evaluated using large process repositories from the healthcare and automotive domain. To demonstrate the feasibility of the refactoring techniques, a proof-of-concept prototype has been implemented