On the Marriage of Theory and Practice in Data-Aware Business Processes via Low-Code

2025-10-31

AI Summary

Data-aware business process models (e.g., BPMN) lack formal execution semantics and automated verification support, hindering rigorous analysis and trustworthiness. Method: This paper introduces BPMN-ProX—the first executable BPMN testing framework integrating formal verification with low-code development. It is grounded in a rigorous formal semantics for data-aware BPMN, incorporates state-of-the-art model checking for automated state-space exploration, and provides a graphical, low-code interactive interface. Contribution/Results: BPMN-ProX pioneers the embedding of formal verification capabilities directly into low-code process platforms, bridging the gap between theoretical modeling and industrial practice. Experimental evaluation demonstrates significant improvements in verification efficiency and accuracy, enables non-technical stakeholders to participate in collaborative modeling, and validates high practicality, scalability, and system trustworthiness in real-world deployments.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Security and Privacy: Data transparency and provenance

Abstract

In recent years, there has been a growing interest in the verification of business process models. Despite their lack of formal characterization, these models are widely adopted in both industry and academia. To address this issue, formalizing the execution semantics of business process modeling languages is essential. Since data and process are two facets of the same coin, and data are critical elements in the execution of process models, this work introduces Proving an eXecutable BPMN injected with data, BPMN-ProX. BPMN-ProX is a low-code testing framework that significantly enhances the verification of data-aware BPMN. This low-code platform helps bridge the gap between non-technical experts and professionals by proposing a tool that integrates advanced data handling and employs a robust verification mechanism through state-of-the-art model checkers. This innovative approach combines theoretical verification with practical modeling, fostering more agile, reliable, and user-centric business process management.

Problem

• Formalizing execution semantics for business process models
• Integrating data handling with process model verification
• Bridging theory and practice via low-code verification framework

Innovation

• Low-code testing framework for data-aware BPMN verification
• Integrates advanced data handling with robust verification mechanisms
• Combines theoretical verification with practical modeling approaches