A Guidelines framework for understandable BPMN models

Corradini F., Ferrari A., Fornari F., Gnesi S., Polini A., Re B., Spagnolo G. O.

BPMN  Business process modeling  Model quality  Modeling guidelines  Models understandability  Tool  Information Systems and Management 

Business process modeling allows abstracting and reasoning on how work is structured within complex organizations. Business process models represent blueprints that can serve different purposes for a variety of stakeholders. For example, business analysts can use these models to better understand how the organization works; employees playing a role in the process can use them to learn the tasks that they are supposed to perform; software analysts/developers can refer to the models to understand the system-as-is before designing the system-to-be. Given the variety of stakeholders that need to interpret these models, and considering the pivotal function that models play within organizations, understandability becomes a fundamental quality that need to be taken into particular account by modelers. In this paper we provide a set of fifty guidelines that can help modelers to improve the understandability of their models. The work focuses on the Business Process Modelling Notation 2.0 standard published by the Object Management Group, which has acquired a clear predominance among the modeling notations for business processes. Guidelines were derived by means of a thoughtful literature review - which allowed identifying around one hundred guidelines - and through successive activities of synthesis and homogenization. In addition, we implemented a freely available open source tool, named BEBoP (understandaBility vErifier for Business Process models), to check the adherence of a model to the guidelines. Finally, guidelines violation has been checked with BEBoP on a dataset of 11,294 models available in a publicly accessible repository. Our tests show that, although the majority of the guidelines are respected by the models, some guidelines, which are recognized as fundamental by the literature, are frequently violated.

Source: Data & knowledge engineering 113 (2018): 129–154. doi:10.1016/j.datak.2017.11.003

Publisher: North-Holland, Amsterdam , Paesi Bassi

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