Jahanbin, Sorour, Kolovos, Dimitris orcid.org/0000-0002-1724-6563 and Gerasimou, Simos (2023) Towards Memory-Efficient Validation of Large XMI Models. In: Proceedings - 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion, MODELS-C 2023. 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2023, 01-06 Oct 2023 Proceedings - 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion, MODELS-C 2023 . Institute of Electrical and Electronics Engineers Inc. , SWE , pp. 241-250.
Abstract
Model validation is a common activity in model-driven engineering, where a model is checked against a set of consistency rules (also referred to as constraints) to assess whether it has desirable properties further to those that can be expressed by the metamodel that it conforms to (e.g. to check that all states in a state machine are reachable or that no classes in an object-oriented model are involved in circular inheritance relationships). Such constraints can be written in general-purpose (e.g. Java) or in task-specific validation languages such as the Object Constraint Language (OCL) or the Epsilon Validation Language (EVL). To check a model that is serialised in the OMG-standard XMI format against a set of constraints, the current state of practice requires loading the entire model into memory first. This can be problematic in cases where loading the model into memory requires more memory (heap space) than is available in the host machine, and is sub-optimal when carrying out distributed model validation over a number of machines. In this paper, we present an approach that uses static analysis to split sets of model validation constraints into sub-groups that operate on smaller subsets of the model. Combined with existing XMI partial loading capabilities, the proposed approach makes it possible to check larger XMI - based models on a single machine and to potentially improve efficiency when checking models in a distributed setting.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Funding Information: ACKNOWLEDGMENT This research is supported by the Lowcomote Training Network, which has received funding from the European Union’s Horizon 2020 Research and InnovationProgram under the Marie Skłodowska-Curiegrant agreement no 813884. © 2023 IEEE. This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy. |
Keywords: | file-based model,memory management,model validation,model-driven engineering,partial loading,XMI model |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION 813884 |
Depositing User: | Pure (York) |
Date Deposited: | 31 Jan 2024 13:00 |
Last Modified: | 16 Oct 2024 11:26 |
Published Version: | https://doi.org/10.1109/MODELS-C59198.2023.00053 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers Inc. |
Series Name: | Proceedings - 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion, MODELS-C 2023 |
Identification Number: | 10.1109/MODELS-C59198.2023.00053 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:208519 |