Steffen Zschaler, Will Barnett, Artur Boronat, Antonio Garcia-Dominguez, Dimitris Kolovos
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引用次数: 0
Abstract
Setting up and configuring model-driven engineering (MDE) tools is not straightforward because the MDE tooling landscape is highly fragmented and because many MDE tools are research prototypes with limited documentation. This creates significant accidental complexity for learners of MDE, who have to overcome installation and configuration hurdles before they can even begin to focus on the core MDE concepts they should be learning. This is further complicated by the complexity of modern MDE tools, which can overwhelm new learners, making it difficult for them to work out what they should do next to achieve a given goal. To address these challenges, we have developed a web-based playground platform that enables learners to engage with MDE learning activities without the need to install anything. The playground metaphor allows teachers to expose only those functionalities directly required for the completion of a particular learning activity. We present the general architecture of the platform, our approach to the declarative integration of new MDE tools, and the way in which teachers can flexibly and declaratively define new MDE learning activities. We have used our platform in a range of different contexts, from live tutorials and 10-week university courses, to developing documentation webpages for MDE tools. We describe examples of such uses, showcasing the flexible configurability of the platform for different types of activities and contexts.
期刊介绍:
We invite authors to submit papers that discuss and analyze research challenges and experiences pertaining to software and system modeling languages, techniques, tools, practices and other facets. The following are some of the topic areas that are of special interest, but the journal publishes on a wide range of software and systems modeling concerns:
Domain-specific models and modeling standards;
Model-based testing techniques;
Model-based simulation techniques;
Formal syntax and semantics of modeling languages such as the UML;
Rigorous model-based analysis;
Model composition, refinement and transformation;
Software Language Engineering;
Modeling Languages in Science and Engineering;
Language Adaptation and Composition;
Metamodeling techniques;
Measuring quality of models and languages;
Ontological approaches to model engineering;
Generating test and code artifacts from models;
Model synthesis;
Methodology;
Model development tool environments;
Modeling Cyberphysical Systems;
Data intensive modeling;
Derivation of explicit models from data;
Case studies and experience reports with significant modeling lessons learned;
Comparative analyses of modeling languages and techniques;
Scientific assessment of modeling practices