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Confluence of aspects for sequence diagrams

机译:时序图方面的融合

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The last decade has seen several aspect language proposals tor UML 2 sequence diagrams. Aspects allow the modeler to define crosscutting concerns of sequence diagrams and to have these woven with the sequence diagrams of a so-called base model, in order to create a woven model. In a real-world scenario, there may be multiple aspects applicable to the same base model. This raises the need to analyse the set of aspects to identify possible aspect interactions (dependencies and conflicts) between applications of aspects. We call a set of aspects terminating if they may not be applied infinitely many times for any given base model. Furthermore, we call a set of terminating aspects confluent, if they, for any given base model, always yield the same final result regardless of the order in which they are applied. Since confluence must hold for any base model, this is a much stronger result than many of the current approaches that have addressed detection of aspect interactions limited to a specific base model. Our aspects are specified using standard sequence diagrams with some extensions. In this paper, we present a confluence theory specialized for our highly expressive aspect language. For the most expressive aspects, we prove that confluence is undecidable. For another class of aspects with considerable expressiveness, we prescribe an algorithm to check confluence. This algorithm is based on what we call an extended critical pair analysis. These results are useful both for modelers and researchers working with sequence diagram aspects and for researchers wanting to establish a confluence theory for other aspect-oriented modelling or model transformation approaches.
机译:在过去的十年中,UML 2序列图已经出现了多个方面的语言建议。方面允许建模者定义顺序图的横切关注点,并将它们与所谓的基础模型的顺序图编织在一起,以创建编织模型。在实际场景中,可能有多个方面适用于同一基本模型。这就需要分析方面的集合,以识别方面的应用程序之间可能存在的方面交互作用(依赖性和冲突)。如果不能将这些方面无限期地应用于任何给定的基础模型,则我们称这些方面为一组。此外,如果它们对于任何给定的基本模型始终产生相同的最终结果,则无论它们应用的顺序如何,我们都将它们称为一组汇合的终结方面。由于汇合对于任何基本模型都必须成立,因此这比许多解决了仅限于特定基本模型的方面交互检测的当前方法要强得多。我们的方面是使用带有一些扩展的标准序列图指定的。在本文中,我们提出了一种融合理论,专门用于高度表达方面的语言。对于最富表现力的方面,我们证明融合是不确定的。对于具有相当高表现力的另一类方面,我们规定了一种检查融合的算法。该算法基于我们所谓的扩展关键对分析。这些结果对于建模者和研究序列图方面的研究人员以及希望为其他面向方面的建模或模型转换方法建立融合理论的研究人员都是有用的。

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