Incremental Model Transformation with Epsilon in Model-Driven Engineering

IF 0.8 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Acta Informatica Pragensia Pub Date : 2022-04-27 DOI:10.18267/j.aip.179
Marzieh Ghorbani, Mohammadreza Sharbaf, B. Zamani
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引用次数: 1

Abstract

Model-Driven Engineering (MDE) is a software development paradigm that uses models as the main artifacts in the development process. MDE uses model transformations to propagate changes between source and target models. In some development scenarios, target models should be updated based on the evolution of source models. In such cases, it is required to re-execute all transformation rules to update the target model. Incremental execution of transformations, which partially executes the transformation rules, is a solution to this problem. The Epsilon Transformation Language (ETL) is a well-known model transformation language that does not support incremental executions. In this paper, we propose an approach to support the incremental execution of ETL transformations. Our proposal includes a process, as well as a prototype, to propagate changes to the target model. In the proposed approach, all the changes in the source model are detected to identify and re-execute the rules which deal with computing the required elements for updating the target model. We evaluated the correctness and performance of our approach by means of a case study. Compared to the standard ETL, the results are promising regarding the correctness of target models as well as faster execution of the transformation.
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模型驱动工程中基于Epsilon的增量模型转换
模型驱动工程(MDE)是一种软件开发范式,它使用模型作为开发过程中的主要工件。MDE使用模型转换在源模型和目标模型之间传播更改。在某些开发场景中,应该根据源模型的演变来更新目标模型。在这种情况下,需要重新执行所有转换规则来更新目标模型。转换的增量执行(部分执行转换规则)就是这个问题的解决方案。Epsilon转换语言(ETL)是一种众所周知的模型转换语言,它不支持增量执行。在本文中,我们提出了一种支持ETL转换增量执行的方法。我们的提案包括一个过程以及一个原型,用于将更改传播到目标模型。在所提出的方法中,检测源模型中的所有变化,以识别并重新执行处理计算更新目标模型所需元素的规则。我们通过案例研究评估了我们方法的正确性和性能。与标准ETL相比,在目标模型的正确性以及转换的更快执行方面,结果是有希望的。
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来源期刊
Acta Informatica Pragensia
Acta Informatica Pragensia Social Sciences-Library and Information Sciences
CiteScore
1.70
自引率
0.00%
发文量
26
审稿时长
12 weeks
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