MARS: A metamodel recovery system using grammar inference

Javed, Faizan; Mernik, Marjan; Gray, Jeff; Bryant, Barrett R.

doi:10.1016/j.infsof.2007.08.003

Cited by 41 publications

(22 citation statements)

References 25 publications

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“…The MARS tool [14] supports metamodel inference from a set of models after migrating or losing their metamodel. This tool relies on a transformation engine, which converts models expressed in XMI to a domain-specific representation, and on an inference engine, which uses grammar inference techniques on the new representation in order to infer the metamodel.…”

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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“…In [9], Javed et al propose an approach to metamodel inference from a set of models after migrating or losing their metamodel. Their semi-automatic approach is based on grammar-schema inference approaches.…”

Section: Metamodel Recovery System (Mars)mentioning

confidence: 99%

“…Their semi-automatic approach is based on grammar-schema inference approaches. Metamodels evolve to adapt to changing requirements and needs of the domain they represent [9]. The instances that conformed to a legacy version of the metamodel can be transformed to adapt to the new metamodel.…”

Section: Metamodel Recovery System (Mars)mentioning

confidence: 99%

Type Inference in Flexible Model-Driven Engineering

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Matragkas

Devlin

et al. 2015

Modelling Foundations and Applications

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Model-driven Engineering (MDE) is an approach to software development that promises increased productivity and product quality. Domain models that conform to metamodels, both of which are the core artefacts in MDE approaches, are manipulated to perform different development processes using specific MDE tools.However, domain experts, who have detailed domain knowledge, typically lack the technical expertise to transfer this knowledge using MDE tools. Flexible or bottom-up Model-driven Engineering is an emerging approach to domain and systems modelling that tackles this challenge by promoting the use of simple drawing tools to increase the involvement of domain experts in MDE processes. In this approach, no metamodel is created upfront but instead the process starts with the definition of example models that will be used to infer a draft metamodel. When complete knowledge of the domain is acquired, a final metamodel is devised and a transition to traditional MDE approaches is possible. However, the lack of a metamodel that encodes the semantics of conforming models and of tools that impose these semantics bears some drawbacks, among others that of having models with nodes that are unintentionally left untyped. In this thesis we propose the use of approaches that use algorithms from three different research areas, that of classification algorithms, constraint programming and graph similarity to help with the type inference of such untyped nodes. We perform an evaluation of the proposed approaches in a number of randomly generated example models from 10 different domains with results suggesting that the approaches could be used for type inference both in an automatic or a semi-automatic style. 3For my parents Despoina and Michalis

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“…For instance, the MARS system [36] enables the recovery of meta-models from repositories of models using grammar inference. The objective is being able to use a set of models after migrating or losing their meta-model.…”

Section: Related Workmentioning

confidence: 99%

Example-driven meta-model development

et al. 2013

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The intensive use of models in ModelDriven Engineering (MDE) raises the need to develop meta-models with different aims, like the construction of textual and visual modelling languages and the specification of source and target ends of model-to-model transformations. While domain experts have the knowledge about the concepts of the domain, they usually lack the skills to build meta-models. Moreover, meta-models typically need to be tailored according to their future usage and specific implementation platform, which demands knowledge available only to engineers with great expertise in specific MDE platforms. These issues hinder a wider adoption of MDE both by domain experts and software engineers.In order to alleviate this situation, we propose an interactive, iterative approach to meta-model construction enabling the specification of example model fragments by domain experts, with the possibility of using informal drawing tools like Dia or yED. These fragments can be annotated with hints about the intention or needs for certain elements. A meta-model is then automatically induced, which can be refactored in an interactive way, and then compiled into an implementation metamodel using profiles and patterns for different platforms and purposes. Our approach includes the use of a virtual assistant, which provides suggestions for improving the meta-model based on well-known refactorings, and a validation mode, enabling the validation of the meta-model by means of examples.

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MARS: A metamodel recovery system using grammar inference

Cited by 41 publications

References 25 publications

Type inference in flexible model-driven engineering using classification algorithms

Type inference in flexible model-driven engineering using classification algorithms

Type Inference in Flexible Model-Driven Engineering

Example-driven meta-model development

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