It is generally agreed that large process models should be decomposed into sub-processes in order to enhance understandability and maintainability. Accordingly, a number of process decomposition criteria and heuristics have been proposed in the literature. This paper presents a review of the field revealing distinct classes of criteria and heuristics. The study raises the question of how different decomposition heuristics affect process model understandability and maintainability. To address this question, an experiment is conducted where two different heuristics, one based on breakpoints and the other on data objects, were used to decompose a flat process model. The results of the experiment show that, although there are minor differences, the heuristics cause very similar results in regard to understandability and maintainability as measured by various process model metrics
