Refactoring large process model repositories

Abstract

Abstract With the increasing adoption of process-aware information systems, large process model repositories have emerged. Typically, the models in such repositories are re-aligned to real-world events and demands through adaptation on a day-to-day basis. This bears the risk of introducing model redundancies and of unnecessarily increasing model complexity. If no continuous investment 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 management 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 techniques 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 refactorings 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.