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Ontology-Based Verification of UML Class Model XOR Constraint and Dependency Relationship Constraints

Asadullah Shaikh1,*, Abdul Hafeez2, M. A. Elmagzoub1, Abdullah Alghamdi1, Ansar Siddique3, Basit Shahzad4

1 Department of Information Systems, Najran University, Najran, 61441, Saudi Arabia
2 Department of Computer Science, SMI University, Karachi, 76400, Pakistan
3 Department of Software Engineering, University of Gujrat, Gujrat, 50700, Pakistan
4 Department of Software Engineering, National University of Modern Languages (NUML), Islamabad, 44020, Pakistan

* Corresponding Author: Asadullah Shaikh. Email: email

(This article belongs to this Special Issue: Soft Computing Methods for Innovative Software Practices)

Intelligent Automation & Soft Computing 2021, 27(2), 565-579.


Unified Modeling Language (UML) models are considered important artifacts of model-driven engineering (MDE). This can automatically transform models to other paradigms and programming languages. If a model has bugs, then MDE can transfer these to a new code. The class model is a key component of UML that is used in analysis and design. Without a formal foundation, UML can create only graphical diagrams, making it impossible to verify properties such as satisfiability, consistency and consequences. Different techniques have been used to verify UML class models, but these do not support some important components. This paper transforms and verifies unsupported components such as XOR association constraints and dependency relationships of a UML class model through ontology. We use various UML class models to validate the proposed ontology-based method, easy and efficient transformation and verification of unsupported elements. The results show this approach can verify large and complex models.


Cite This Article

A. Shaikh, A. Hafeez, M. A. Elmagzoub, A. Alghamdi, A. Siddique et al., "Ontology-based verification of uml class model xor constraint and dependency relationship constraints," Intelligent Automation & Soft Computing, vol. 27, no.2, pp. 565–579, 2021.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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