An Extensible Meta-Modelling Assistant

Mora-Segura, Ángel; Pescador, Ana; Lara, Juan de; Wimmer, Manuel

doi:10.1109/edoc.2016.7579377

Cited by 15 publications

(16 citation statements)

References 23 publications

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“…The analysed papers include approaches to query repositories and suggest relevant artefacts for models and meta-models, but not for transformations or code generators. Extremo [123][124][125] is an extensible toolindependent assistant that helps finding relevant information for models and meta-models out of heterogeneous data sources (e.g. ontologies, XML schemas, RDF data, meta-models), and the results are ranked according to their suitability for the user.…”

Section: Complete Most Approaches Whose Purpose Is Completingmentioning

confidence: 99%

“…For this purpose, they are frequently defined over a meta-modelling frameworklike the Eclipse Modeling Framework (EMF) [131], Kermeta [57], GME [74], or DPF [109]-which enables their application to models of any language defined within the framework. This is the case of [11,[34][35][36]53,68,69,86,91,93,98,99,108,[123][124][125][126][127]130]. In the case of DiaGen [82], language independence is achieved by representing models as hypergraphs, and language definitions as hypergraph grammars.…”

Section: Complete Most Approaches Whose Purpose Is Completingmentioning

confidence: 99%

“…In the case of DiaGen [82], language independence is achieved by representing models as hypergraphs, and language definitions as hypergraph grammars. Other approaches are meta-level independent, and since metamodels are also models, such approaches are suitable for both meta-models and models [5,6,11,[34][35][36]53,68,69,[123][124][125]130]; however, when applied to meta-models, the approaches are dependent on the meta-modelling language used, like EMF's Ecore. Finally, language independence can be a gradual term.…”

Section: Complete Most Approaches Whose Purpose Is Completingmentioning

confidence: 99%

“…Extremo [123][124][125] is a modelling assistant that defines extension points (the extensibility mechanism provided by Eclipse) to allow its integration with external modelling and meta-modelling tools within Eclipse. Finally, Hermes [34][35][36] is not a concrete RS but a framework with a plugin-based architecture to develop RSs within Eclipse.…”

Section: Maturitymentioning

confidence: 99%

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Recommender systems in model-driven engineering

et al. 2021

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Recommender systems are information filtering systems used in many online applications like music and video broadcasting and e-commerce platforms. They are also increasingly being applied to facilitate software engineering activities. Following this trend, we are witnessing a growing research interest on recommendation approaches that assist with modelling tasks and model-based development processes. In this paper, we report on a systematic mapping review (based on the analysis of 66 papers) that classifies the existing research work on recommender systems for model-driven engineering (MDE). This study aims to serve as a guide for tool builders and researchers in understanding the MDE tasks that might be subject to recommendations, the applicable recommendation techniques and evaluation methods, and the open challenges and opportunities in this field of research.

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Section: Complete Most Approaches Whose Purpose Is Completingmentioning

confidence: 99%

Section: Complete Most Approaches Whose Purpose Is Completingmentioning

confidence: 99%

Section: Complete Most Approaches Whose Purpose Is Completingmentioning

confidence: 99%

Section: Maturitymentioning

confidence: 99%

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Recommender systems in model-driven engineering

et al. 2021

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“…In comparison with our previous work [11], we provide extensions for a set of different technical spaces that include constraint interpreters and an exten-58 sible query mechanism. Moreover, Extremo's internal data model has been 59 extended to handle level-agnostic information, i.e., for an arbitrary number 60 of meta-levels.…”

Section: Introductionmentioning

confidence: 99%

Automated modelling assistance by integrating heterogeneous information sources

Mora-Segura

Lara

Neubauer

et al. 2018

Computer Languages, Systems & Structures

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Model-Driven Engineering (MDE) uses models as its main assets in the software development process. The structure of a model is described through a metamodel. Even though modelling and meta-modelling are recurrent activities in MDE and a vast amount of MDE tools exist nowadays, they are tasks typically performed in an unassisted way. Usually, these tools cannot extract useful knowledge available in heterogeneous information sources like XML, RDF, CSV or other models and meta-models. We propose an approach to provide modelling and meta-modelling assistance. The approach gathers heterogeneous information sources in various technological spaces, and represents them uniformly in a common data model. This enables their uniform querying, by means of an extensible mechanism, which can make use of services, e.g., for synonym search and word sense analysis. The query results can then be easily incorporated into the (meta-)model being built. The approach has been realized in the Extremo tool, developed as an Eclipse plugin. Extremo has been validated in the context of two domains-production systems and process modelling-taking into account a large and complex industrial standard for classification and product description. Further validation results indicate that the integration of Extremo in various modelling environments can be achieved with low effort, and that the tool is able to handle information from most existing technological spaces.

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ModelSet: a dataset for machine learning in model-driven engineering

López

Izquierdo²,

Cuadrado

2021

Softw Syst Model

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The application of machine learning (ML) algorithms to address problems related to model-driven engineering (MDE) is currently hindered by the lack of curated datasets of software models. There are several reasons for this, including the lack of large collections of good quality models, the difficulty to label models due to the required domain expertise, and the relative immaturity of the application of ML to MDE. In this work, we present ModelSet, a labelled dataset of software models intended to enable the application of ML to address software modelling problems. To create it we have devised a method designed to facilitate the exploration and labelling of model datasets by interactively grouping similar models using off-the-shelf technologies like a search engine. We have built an Eclipse plug-in to support the labelling process, which we have used to label 5,466 Ecore meta-models and 5,120 UML models with its category as the main label plus additional secondary labels of interest. We have evaluated the ability of our labelling method to create meaningful groups of models in order to speed up the process, improving the effectiveness of classical clustering methods. We showcase the usefulness of the dataset by applying it in a real scenario: enhancing the MAR search engine. We use ModelSet to train models able to infer useful metadata to navigate search results. The dataset and the tooling are available at https://figshare.com/s/5a6c02fa8ed20782935c and a live version at http://modelset.github.io.

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An Extensible Meta-Modelling Assistant

Cited by 15 publications

References 23 publications

Recommender systems in model-driven engineering

Recommender systems in model-driven engineering

Automated modelling assistance by integrating heterogeneous information sources

ModelSet: a dataset for machine learning in model-driven engineering

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