When Language Meets Language: Anti Patterns Resulting from Mixing Natural and Modeling Language

Abstract

Business process modeling has become an integral part of many organizations for documenting and redesigning complex organizational operations. However, the increasing size of process model repositories calls for automated quality assurance techniques. While many aspects such as formal and structural problems are well understood, there is only a limited understanding of semantic issues caused by natural language. One particularly severe problem arises when modelers employ natural language for expressing control-flow constructs such as gateways or loops. This may not only negatively affect the understandability of process models, but also the performance of analysis tools, which typically assume that process model elements do not encode control-flow related information in natural language. In this paper, we aim at increasing the current understanding of mixing natural and modeling language and therefore exploratively investigate three process model collections from practice. As a result, we identify a set of nine anti patterns for mixing natural and modeling language.