Ontology-based Detection of Inconsistencies in UML/OCL Models

Shan Lu, Alexey Tazin, Yanji Chen, Mieczyslaw Kokar, Jeff Smith

2022

Abstract

Consistency checking of UML/OCL models is a challenging issue in software development. In this paper, we discuss an OWL/ontology-based method to detect the inconsistencies in the UML/OCL models as the first step of requirement change management. Specifically, we map the UML/OCL models to OWL, so that the consistency of the corresponding ontology can be checked by OWL reasoners automatically. We propose a set of mapping rules to interpret the components of UML state machine diagrams, along with OCL constraints, to OWL DL. Towards this objective, we demonstrate three consistency reasoning tasks over a state machine diagram using OWL reasoners. In each case, the result of reasoning is accompanied by an explanation of the logic behind the decision.

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Paper Citation


in Harvard Style

Lu S., Tazin A., Chen Y., Kokar M. and Smith J. (2022). Ontology-based Detection of Inconsistencies in UML/OCL Models. In Proceedings of the 10th International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD, ISBN 978-989-758-550-0, pages 194-202. DOI: 10.5220/0010814500003119


in Bibtex Style

@conference{modelsward22,
author={Shan Lu and Alexey Tazin and Yanji Chen and Mieczyslaw Kokar and Jeff Smith},
title={Ontology-based Detection of Inconsistencies in UML/OCL Models},
booktitle={Proceedings of the 10th International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD,},
year={2022},
pages={194-202},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010814500003119},
isbn={978-989-758-550-0},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD,
TI - Ontology-based Detection of Inconsistencies in UML/OCL Models
SN - 978-989-758-550-0
AU - Lu S.
AU - Tazin A.
AU - Chen Y.
AU - Kokar M.
AU - Smith J.
PY - 2022
SP - 194
EP - 202
DO - 10.5220/0010814500003119