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Article type: Research Article
Authors: Zhou, Dongdaia; b; c | Chen, Xuea | Jin, Qiuzhia | Kuang, Zhejuna; * | Yang, Hongjid
Affiliations: [a] College of Information and Software Engineering, Northeast Normal University, Changchun 130117, Jilin, China | [b] E-Learning Technological Innovation Center of Jilin Province, Changchun 130117, Jilin, China | [c] Engineering and Research Center of E-learning, Ministry of Education, Changchun 130117, Jilin, China | [d] Centre for Creative Computing, Bath Spa University, Bath, UK
Correspondence: [*] Corresponding author: Zhejun Kuang, College of Information and Software Engineering, Northeast Normal University, Changchun 130117, Jilin, China. E-mail: kuangzj100@nenu.edu.cn.
Abstract: To tackle the problems such as the imperfection and inconsistency in software requirements in traditional Computation Independent Model (CIM) modelling, the low degree of automation as well as the imperfection in the description of Platform Independent Model (PIM) in CIM-to-PIM transforming, in this article, we propose a Business-Process-based CIM modelling method and a CIM-to-PIM transformation approach. Business Process Model is used to express CIM, and UML’s Sequence Diagram, State Chart Diagram as well as Class Diagram are used to express PIM. Firstly, the users’ requirements are obtained through business process models. We extract use cases from business processes and create use case specifications. A verification mechanism is also added for the use case specification. Secondly, we transform CIMs into PIMs automatically with use case specifications as the inputs as well as combining with use case based thinking, responsibility based thinking and affinity analysis. Finally, by comparing with the methods in other studies, we conclude that methods proposed in this article can ensure model integrity and increase the degree of model transformation automation.
Keywords: MDA, CIM modelling, business process, CIM-to-PIM transformation, affinity analysis
DOI: 10.3233/MGS-170271
Journal: Multiagent and Grid Systems, vol. 13, no. 3, pp. 269-286, 2017
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