Model Transformation By-Example: A Survey of the First Wave

Kappel, Gerti; Langer, Philip; Retschitzegger, Werner; Schwinger, Wieland; Wimmer, Manuel

doi:10.1007/978-3-642-28279-9_15

Cited by 63 publications

(43 citation statements)

References 36 publications

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“…These approaches rely on user-defined examples of input and output models, and the inference is based on various techniques such as metaheuristics, model comparison and induction. A literature survey, which summarises the research in this area, is provided in [16].…”

Section: Related Workmentioning

confidence: 99%

Type inference in flexible model-driven engineering using classification algorithms

et al. 2018

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Flexible or bottom-up model-driven engineering (MDE) is an emerging approach to domain and systems modelling. Domain experts, who have detailed domain knowledge, typically lack the technical expertise to transfer this knowledge using traditional MDE tools. Flexible MDE approaches tackle this challenge by promoting the use of simple drawing tools to increase the involvement of domain experts in the language definition process. In such approaches, no metamodel is created upfront, but instead the process starts with the definition of example models that will be used to infer the metamodel. Pre-defined metamodels created by MDE experts may miss important concepts of the domain and thus restrict their expressiveness. However, the lack of a metamodel, that encodes the semantics of conforming models has some drawbacks, among others that of having models with elements that are unintentionally left untyped. In this paper, we propose the use of classification algorithms to help with the inference of such untyped elements. We evaluate the proposed approach in a number of random generated example models from various domains. The correct type prediction varies from 23 to 100% depending on the domain, the proportion of elements that were left untyped and the prediction algorithm used.

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Section: Related Workmentioning

confidence: 99%

Type inference in flexible model-driven engineering using classification algorithms

et al. 2018

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“…Regarding the first design choice, "by-demonstration" techniques have been applied to several MDE artefacts, like model transformations [8], but their use is not so common to describe graphical modelling environments. The closest work to ours is [2], which describes a system atop Microsoft Visio to derive DSLs by demonstration.…”

Section: Related Workmentioning

confidence: 99%

Example-Based Generation of Graphical Modelling Environments

López-Fernández

Garmendía

Guerra

et al. 2016

Modelling Foundations and Applications

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El acceso a la versión del editor puede requerir la suscripción del recurso Access to the published version may require subscriptionExample-based generation of graphical modelling environmentsJesús J. López-Fernández, Antonio Garmendia, Esther Guerra, Juan de LaraAbstract. Domain-Specific Languages (DSLs) present numerous benefits like powerful domain-specific primitives, an intuitive syntax for domain experts, and the possibility of advanced code generation for narrow domains. While a graphical syntax is sometimes desired for a DSL, constructing graphical modelling environments is a costly and highly technical task. This relegates domain experts to play a passive role in their development and hinders a wider adoption of graphical DSLs. Targeting a simpler DSL construction process, we propose an examplebased technique for the automatic generation of modelling environments for graphical DSLs. This way, starting from examples of the DSL likely provided by domain experts using drawing tools like yED, our system is able to synthesize a graphical modelling environment that mimics the syntax of the provided examples. This includes a meta-model for the abstract syntax of the DSL, and a graphical concrete syntax supporting spatial relationships like containment or attachment. The system is implemented as an Eclipse plugin, and we demonstrate its usage on a running example in the home networking domain.

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“…Therefore new solutions are proposed to improve the process and provide an alternative method that improves the efficiency and accuracy of the transformations. In this chapter ideas on how current situation in specific situations can be improved using advanced techniques, such as grammar-based model transformations [21] and model transformation by-example [22][23][24][25] element identification are presented.…”

Section: Assumptions For Model To Model Transformation Improvementmentioning

confidence: 99%

Content Based Model Transformations: Solutions to Existing Issues with Application in Information Security

Janulevičius

Ramanauskaitė

Goranin

et al. 2016

INT J COMPUT COMMUN

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Model-Driven Engineering uses models in various stages of the software engineering. To reduce the cost of modelling and production, models are reused by transforming. Therefore the accuracy of model transformations plays a key role in ensuring the quality of the process. However, problems exist when trying to transform a very abstract and content dependent model. This paper describes the issues arising from such transformations. Solutions to solve problems in content based model transformation are proposed as well. The usage of proposed solutions allowing realization of semi-automatic transformations was integrated into a tool, designed for OPC/XML drawing file transformations to CySeMoL models. The accuracy of transformations in this tool has been analyzed and presented in this paper to acquire data on the proposed solutions influence to the accuracy in content based model transformation.

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Model Transformation By-Example: A Survey of the First Wave

Cited by 63 publications

References 36 publications

Type inference in flexible model-driven engineering using classification algorithms

Type inference in flexible model-driven engineering using classification algorithms

Example-Based Generation of Graphical Modelling Environments

Content Based Model Transformations: Solutions to Existing Issues with Application in Information Security

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