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.

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