With the intention of design by reuse,
configurable process models
provide a way to model variability in reference models that need to be configured according to specific needs. Recently, the increasing adoption of configurable process models has resulted in a large number of configured process variants. Current research activities are successfully investigating the design and configuration of configurable process models. However, a little attention is attributed to analyze the way they are configured. Such analysis can yield useful information in order to help organizations improving the quality of their configurable process models. In this paper, we introduce
configuration rule mining
, a frequency-based approach for supporting the variability analysis in configurable process models. Basically, we propose to enhance configurable process models with configuration rules that describe the interrelationships between the frequently selected configurations. These rules are extracted from a large collection of process variants using
association rule mining
techniques. To show the feasibility and effectiveness of our approach, we conduct experiments on a dataset from SAP reference model.