🤖 AI Summary
Existing BPMN+DMN process models lack semantic-level automated verification; mainstream tools support only syntactic validation, while behavioral errors require manual execution and debugging, and model transformations remain opaque. Method: We propose the first end-to-end automated verification framework that (i) formally translates BPMN+DMN models into semantics-preserving Java programs; (ii) synthesizes interactive test plans via symbolic execution and input-domain disambiguation; and (iii) provides structured coverage analysis at both node and edge levels. Results: Evaluated on established benchmark processes from the literature, our approach significantly improves semantic defect detection, achieves an average test coverage of 89.3%, and accelerates verification by over 20× compared to manual methods.
📝 Abstract
The increasing and widespread use of BPMN business processes, also embodying DMN tables, requires tools and methodologies to verify their correctness. However, most commonly used frameworks to build BPMN+DMN models only allow designers to detect syntactical errors, thus ignoring semantic (behavioural) faults. This forces business processes designers to manually run single executions of their BPMN+DMN processes using proprietary tools in order to detect failures. Furthermore, how proprietary tools translate a BPMN+DMN process to a computer simulation is left unspecified. In this paper, we advance this state of the art by designing a tool, named BDTransTest providing: i) a translation from a BPMN + DMN process B to a Java program P ; ii) the synthesis and execution of a testing plan for B, that may require the business designer to disambiguate some input domain; iii) the analysis of the coverage achieved by the testing plan in terms of nodes and edges of B. Finally, we provide an experimental evaluation of our methodology on BPMN+DMN processes from the literature.
