Esta es la versión de autor del artículo publicado en: This is an author produced version of a paper published in: AbstractThe integration of usable and flexible analysis support in modelling environments is a key success factor in Model-Driven Development. In this paradigm, models are the core asset from which code is automatically generated, and thus ensuring model correctness is a fundamental quality control activity. For this purpose, a common approach consists on transforming the system models into formal semantic domains for verification. However, if the analysis results are not shown in a proper way to the end-user (e.g. in terms of the original language) they may become useless.In this paper we present a novel DSVL called BaVeL that facilitates the flexible annotation of verification results obtained in semantic domains to different formats, including the context of the original language. BaVeL is used in combination with a consistency framework, providing support for all the verification life cycle: acquisition of additional input data, transformation of the system models into semantic domains, verification, and flexible annotation of analysis results.The approach has been empirically validated by its implementation in the AToM 3 meta-modelling tool, and tested with several DSVLs. In this paper we present a case study for the analysis of a notation in the area of Digital Libraries, where the analysis is performed by transformations into Petri nets and a process algebra.
Abstract. Meta-modelling is at the core of Model-Driven Engineering, where it is used for language engineering and domain modelling. The OMG's Meta-Object Facility is the standard framework for building and instantiating meta-models. However, in the last few years, several researchers have identified limitations and rigidities in such a scheme, most notably concerning the consideration of only two meta-modelling levels at the same time.In this paper we present MetaDepth, a novel framework that supports a dual linguistic/ontological instantiation and permits building systems with an arbitrary number of meta-levels through deep metamodelling. The framework implements advanced modelling concepts allowing the specification and evaluation of derived attributes and constraints across multiple meta-levels, linguistic extensions of ontological instance models, transactions, and hosting different constraint and action languages.
Esta es la versión de autor del artículo publicado en: This is an author produced version of a paper published in: AbstractIn this paper we propose a method to derive OCL invariants from declarative model-to-model transformations in order to enable their verification and analysis. For this purpose we have defined a number of invariant-based verification properties which provide increasing degrees of confidence about transformation correctness, such as whether a rule (or the whole transformation) is satisfiable by some model, executable or total. We also provide some heuristics for generating meaningful scenarios that can be used to semiautomatically validate the transformations. As a proof of concept, the method is instantiated for two prominent model-to-model transformation languages: Triple Graph Grammars and QVT.
Model-Driven Engineering (MDE) promotes models as the primary artefacts in the software development process, from which code for the final application is derived. Standard approaches to MDE (like those based on MOF or EMF) advocate a two-level metamodelling setting where Domain-Specific Modelling Languages (DSMLs) are defined through a metamodel that is instantiated to build models at the metalevel below. Multilevel modelling (also called deep metamodelling ) extends the standard approach to metamodelling by enabling modelling at an arbitrary number of metalevels, not necessarily two. Proposers of multilevel modelling claim this leads to simpler model descriptions in some situations, although its applicability has been scarcely evaluated. Thus, practitioners may find it difficult to discern when to use it and how to implement multilevel solutions in practice. In this article, we discuss those situations where the use of multilevel modelling is beneficial, and identify recurring patterns and idioms. Moreover, in order to assess how often the identified patterns arise in practice, we have analysed a wide range of existing two-level DSMLs from different sources and domains, to detect when their elements could be rearranged in more than two metalevels. The results show this scenario is not uncommon, while in some application domains (like software architecture and enterprise/process modelling) pervasive, with a high average number of pattern occurrences per metamodel.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.