Toward automated verification of timed business process models using timed-automata networks and temporal properties

IF 8.1 1区 计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS
Chanon Dechsupa, Wiwat Vatanawood, Arthit Thongtak
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引用次数: 0

Abstract

This paper addresses the challenges in verifying the correctness and effectiveness of business process models with time constraints by introducing an automated approach that combines formal model abstraction with Time Computation Tree Logic (TCTL) property expression. The proposed framework transforms BPMN models into UPPAAL timed-automata networks, utilizing structural process block patterns to generate TCTL property expressions for verification. Key innovations include automated transformation and verification processes that significantly reduce time and effort compared to traditional methods. The transformation rules and generated TCTL properties are comprehensively tested, and the correctness and effectiveness of the derived UPPAAL constructs are validated through extensive simulations and real-world case studies in the UPPAAL-TIGA environment. Results demonstrate the practical applicability of this approach, with substantial improvements in verification efficiency and accuracy and a notable reduction in the time needed for model abstraction and TCTL property expression. The framework includes rules for transforming block timed BPMN models into UPPAAL and logic for automatically generating temporal properties, highlighting its potential to enhance business process modeling and verification practices.
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来源期刊
Information Sciences
Information Sciences 工程技术-计算机:信息系统
CiteScore
14.00
自引率
17.30%
发文量
1322
审稿时长
10.4 months
期刊介绍: Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.
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