Detecting Data-Flow Errors in BPMN 2.0

Abstract

Data-flow errors in BPMN 2.0 process models, such as missing or unused data, lead to undesired process executions. In particular, since BPMN 2.0 with a standardized execution semantics allows specifying alternatives for data as well as optional data, identifying missing or unused data systematically is difficult. In this paper, we propose an approach for detecting data-flow errors in BPMN 2.0 process models. We formalize BPMN process models by mapping them to Petri Nets and unfolding the execution semantics regarding data. We define a set of anti-patterns representing data-flow errors of BPMN 2.0 process models. By employing the anti-patterns, our tool performs model checking for the unfolded Petri Nets. The evaluation shows that it detects all data-flow errors identified by hand, and so improves process quality.