Playground for multi-level modeling constructs

Software and Systems Modeling(2021)

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摘要
In recent years, multi-level modeling has become more and more popular. It is mainly due to the fact that multi-level modeling aims to reduce or even totally eliminate any accidental complexity inadvertently created as by-product in traditional model design. Moreover, besides reducing model complexity, multi-level modeling also improves on general comprehension of models. The key enablers of multi-level modeling are the concepts of clabjects and deep instantiation. The latter is often governed by the potency notion, of which many different interpretations and variations emerged over the years. However, there exist also some approaches that disregard the potency notion. Thus, multi-level modeling approaches tend to take advantage of different theoretical and practical backgrounds. In this paper, we propose a unifying framework, the Multi-Level Modeling Playground (MLMP), which is a validating modeling environment for multi-level modeling research. The MLMP environment is based on our multi-layer modeling framework (the Dynamic Multi-Layer Algebra), which provides useful mechanisms to validate different multi-level modeling constructs. Since beyond the structure also the well-formedness rules of the modeling constructs can be specified, our proposed MLMP environment delivers several practical benefits: i) well-formedness is always verified, ii) multi-level constructs can be experimented with independently of any concrete tool chains, and iii) relationships (i.e., correlations or exclusions) between different multi-level constructs can be easily investigated in practice. Also, the capability of the environment is demonstrated via complete examples inspired by state-of-the-art research literature.
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关键词
Multi-level modeling, Potency notion, Clabject, Level-blind, Scientific experimentation, Modular playground
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