Extremo: An Eclipse plugin for modelling and meta-modelling assistance
Modelling is a core activity in software development paradigms like Model-driven Engineering (MDE). Therefore, the quality of (meta-)models is crucial for the success of software projects. However, many times, modelling becomes a purely manual activity, which does not take advantage of information e...
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Published in | Science of computer programming Vol. 180; pp. 71 - 80 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Elsevier B.V
01.07.2019
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Subjects | |
Online Access | Get full text |
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Summary: | Modelling is a core activity in software development paradigms like Model-driven Engineering (MDE). Therefore, the quality of (meta-)models is crucial for the success of software projects. However, many times, modelling becomes a purely manual activity, which does not take advantage of information embedded in heterogeneous information sources, such as XML documents, ontologies, or other models and meta-models.
In order to improve this situation, we present Extremo, an Eclipse plugin aimed at gathering the information stored in heterogeneous sources in a common data model, to facilitate the reuse of information chunks in the model being built. The tool covers the steps needed to incorporate this knowledge within an external modelling tool, supporting the uniform query of the heterogeneous sources and the evaluation of constraints. Flexibility of the main features (e.g., supported data formats, queries) is achieved by means of extensible mechanisms. To illustrate the usefulness of Extremo, we describe a practical case study in the financial domain and evaluate its performance and scalability.
•We describe an assistant called Extremo to help in the (meta-)modelling process.•Extremo gathers heterogeneous information sources, allowing their reuse in models.•Extremo supports querying the heterogeneous information sources in a uniform way.•Extremo is extensible, enabling the addition of new data formats and types of queries. |
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ISSN: | 0167-6423 1872-7964 |
DOI: | 10.1016/j.scico.2019.05.003 |