Towards measuring process model granularity via natural language analysis

Abstract

Nowadays business process modeling is an integral part of many organizations to document and redesign complex organizational processes. Particularly due to the large number of process models, quality assurance represents an important issue in many organizations. While many quality aspects are well understood and can be automatically checked with existing tools, there is currently no possibility to support modelers in maintaining a consistent degree of granularity. In this paper, we leverage natural language analysis in process models to introduce a novel set of metrics that indicate the granularity of process models. We evaluate the proposed metrics using two hierarchically organized process model collections from practice. Statistical tests demonstrate the expressive power of the proposed metrics. © Springer International Publishing Switzerland 2014.