Source of Publication
ICSOFT 2017 - Proceedings of the 12th International Conference on Software Technologies
Copyright © 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved. Similar to software products, the quality of a Business Process model is vital to the success of all the phases of its lifecycle. Indeed, a high quality BP model paves the way to the successful implementation, execution and performance of the business process. In the literature, the quality of a BP model has been assessed through either the application of formal verification, or most often the evaluation of quality metrics calculated in the static and/or simulated model. Each of these assessment means addresses different quality characteristics and meets particular analysis needs. In this paper, we adopt metrics-based assessment to evaluate the quality of business process models, modeled with Business Process Modeling and Notation (BPMN), in terms of their comprehensibility and modifiability. We propose a fuzzy logic-based approach that uses existing quality metrics for assessing the attainment level of these two quality characteristics. By analyzing the static model, the proposed approach is easy and fast to apply. In addition, it overcomes the threshold determination problem by mining a repository of BPMN models. Furthermore, by relying on fuzzy logic, it resembles human reasoning during the evaluation of the quality of business process models. We illustrate the approach through a case study and its tool support system developed under the eclipse framework. The preliminary experimental evaluation of the proposed system shows encouraging results.
Business | Computer Sciences
BPMN, Business process, Model quality, Quality metrics and fuzzy logic
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Yahya, Fadwa; Boukadi, Khouloud; Abdallah, Hanêne Ben; and Maamar, Zakaria, "A fuzzy logic-based approach for assessing the quality of business process models" (2017). All Works. 119.
Indexed in Scopus
Open Access Type
Hybrid: This publication is openly available in a subscription-based journal/series