Dissertação para obtenção do Grau de Doutor em
Engenharia InformáticaContext: The BPMN 2.0 specification contains the rules regarding the correct usage of
the language’s constructs. Practitioners have also proposed best-practices for producing better BPMN models. However, those rules are expressed in natural language, yielding sometimes ambiguous interpretation, and therefore, flaws in produced BPMN models.
Objective: Ensuring the correctness of BPMN models is critical for the automation of
processes. Hence, errors in the BPMN models specification should be detected and
corrected at design time, since faults detected at latter stages of processes’ development can be more costly and hard to correct. So, we need to assess the quality of BPMN models in a rigorous and systematic way.
Method: We follow a model-driven approach for formalization and empirical validation
of BPMN well-formedness rules and BPMN measures for enhancing the quality of
BPMN models.
Results: The rule mining of BPMN specification, as well as recently published BPMN works, allowed the gathering of more than a hundred of BPMN well-formedness and
best-practices rules. Furthermore, we derived a set of BPMN measures aiming to provide information to process modelers regarding the correctness of BPMN models. Both BPMN rules, as well as BPMN measures were empirically validated through samples of
BPMN models.
Limitations: This work does not cover control-flow formal properties in BPMN models, since they were extensively discussed in other process modeling research works.
Conclusion: We intend to contribute for improving BPMN modeling tools, through the
formalization of well-formedness rules and BPMN measures to be incorporated in those
tools, in order to enhance the quality of process modeling outcomes