Community-driven language development

Izquierdo, M.S.; Cabot, Jordi

doi:10.1109/mise.2012.6226011

Cited by 14 publications

(10 citation statements)

References 16 publications

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“…In the process of building the domain knowledge needed to define a system, a business process or a Domain-Specific Language (DSL) the participation of all the stakeholders is important [5,[22][23][24][25]. They hold the knowledge of the domain and they know the concepts that need to be modelled.…”

Section: Motivation and Backgroundmentioning

confidence: 99%

“…While this may be an easy or at least understandable process for MDE experts, this is not always the case with domain experts [5] who are more familiar with tools like simple drawing editors [22]. However, the involvement of domain experts is important in the definition of high quality and well-defined DSLs (i.e., those that cover all the needed aspects of a domain) [5,24,25,30]. To address the aforementioned issue, flexible modelling approaches have been proposed in the literature (e.g., [4][5][6]31]).…”

Section: Bottom-up Mdementioning

confidence: 99%

“…Thus, the active collaboration among all of them is important; the participation of the stakeholders, which are usually the domain experts, is of interest as it can lead to better understanding of the domain and consequently to complete and valid products [5,[22][23][24][25]. Although MDE conceals low level implementation details, focusing on abstractions, the active participation of the domain experts is not always achieved.…”

Section: Introductionmentioning

confidence: 99%

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Type Inference in Flexible Model-Driven Engineering

Zolotas

Matragkas

Devlin

et al. 2015

Lecture Notes in Computer Science

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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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Section: Motivation and Backgroundmentioning

confidence: 99%

Section: Bottom-up Mdementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Type Inference in Flexible Model-Driven Engineering

Zolotas

Matragkas

Devlin

et al. 2015

Lecture Notes in Computer Science

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“…Integrating end-users in the meta-model construction process has also been regarded as a means to improve the quality of the resulting meta-model. In [11], the authors propose a collaborative approach to meta-model construction which involves both domain and technical experts. The approach is supported by a DSL to represent the collaborations among stakeholders (change proposals, solution proposals or comments) while the metamodel is being developed.…”

Section: Related Workmentioning

confidence: 99%

Bottom-Up Meta-Modelling: An Interactive Approach

Cuadrado

Lara

Guerra

2012

Lecture Notes in Computer Science

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Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: Abstract. The intensive use of models in Model-Driven 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. These should be tailored according to their future usage and specific implementation platform, which demands knowledge available only to engineers with great expertise in 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 model fragments by domain experts, with the possibility of using informal drawing tools like Dia. These fragments can be annotated with hints about the intention or needs for certain elements. A meta-model is automatically induced, which can be refactored in an interactive way, and then compiled into an implementation meta-model using profiles and patterns for different platforms and purposes.

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“…Flexible modelling is arguably more accessible to domain experts as the latter can use tools that they are already familiar with to express the concepts of the domain; the involvement of domain experts is widely argued to be important in the definition of highquality DSLs [12,13,23,35]. In this fashion, modellers work without being restricted by a metamodel, which may be defined by MDE experts who are not necessarily domain experts.…”

Section: Introductionmentioning

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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Community-driven language development

Cited by 14 publications

References 16 publications

Type Inference in Flexible Model-Driven Engineering

Type Inference in Flexible Model-Driven Engineering

Bottom-Up Meta-Modelling: An Interactive Approach

Type inference in flexible model-driven engineering using classification algorithms

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