About: Generating Large EMF Models Efficiently: A Rule-Based, Configurable Approach

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Generating Large EMF Models Efficiently: A Rule-Based, Configurable Approach
Creator	Kehrer, Timo Kosiol, Jens Taentzer, Gabriele Nassar, Nebras
Source	PMC
abstract	There is a growing need for the automated generation of instance models to evaluate model-driven engineering techniques. Depending on a chosen application scenario, a model generator has to fulfill different requirements: As a modeling language is usually defined by a meta-model, all generated models are expected to conform to their meta-models. For performance tests of model-driven engineering techniques, the efficient generation of large models should be supported. When generating several models, the resulting set of models should show some diversity. Interactive model generation may help in producing relevant models. In this paper, we present a rule-based, configurable approach to automate model generation which addresses the stated requirements. Our model generator produces valid instance models of meta-models with multiplicities conforming to the Eclipse Modeling Framework (EMF). An evaluation of the model generator shows that large EMF models (with up to half a million elements) can be produced. Since the model generation is rule-based, it can be configured beforehand or during the generation process to produce sets of models that are diverse to a certain extent.
has issue date	2020-03-13(xsd:dateTime)
bibo:doi	10.1007/978-3-030-45234-6_11
has license	cc-by
sha1sum (hex)	9320d804157389c92fd0a3de5b01e2fb8b78a413
schema:url	https://doi.org/10.1007/978-3-030-45234-6_11
resource representing a document's title	Generating Large EMF Models Efficiently: A Rule-Based, Configurable Approach
has PubMed Central identifier	PMC7418137
schema:publication	Fundamental Approaches to Software Engineering
resource representing a document's body	covid:9320d804157389c92fd0a3de5b01e2fb8b78a413#body_text
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