"Union is power": analyzing families of goal models using union models
"Union is power": analyzing families of goal models using union models
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“联盟就是力量”：使用联盟模型分析目标模型族
DOI：
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发表时间：
2020
期刊：
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通讯作者：
Daniel Amyot
中科院分区：
文献类型：
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作者：
Sanaa A. Alwidian;Daniel Amyot
目标模型族是一组相关的目标模型，它们符合相同的元模型，在模型之间具有共性和可变性。这些族源于初始模型随时间演变成几个版本和/或模型在空间维度上的变化（例如，产品）。在存在目标模型的若干版本/变型的上下文中，分析具有典型相似性的一组相关模型（一次一个模型）通常涉及冗余计算并且可能需要重复的用户辅助（例如，用于交互式分析）和费力的活动。本文提出使用联合模型作为第一类工件来分析目标模型族，以提高特定语言分析过程的性能。本文根据经验评估的性能增益所产生的适应（或解除）现有的分析技术，具体到面向目标的需求语言（GRL）的GRL模型的家庭，所有在一次使用一个工会模型，分析个别车型相比。我们的实验表明，使用IBM CPLEX优化器的基础上，使用工会模型进行计算昂贵的分析，即定量反向传播，家庭的GRL模型的有用性和性能收益。
A goal model family is a set of related goal models that conform to the same metamodel, with commonalities and variabilities between models. Such families stem from the evolution of initial models into several versions over time and/or the variation of models over the space dimension (e.g., products). In contexts where there are several versions/variations of a goal model, analyzing a set of related models with typical similarities, one model at a time, often involves redundant computations and may require repeated user assistance (e.g., for interactive analysis) and laborious activities. This paper proposes the use of union models as first-class artifacts to analyze families of goal models, in order to improve performance of language-specific analysis procedures. The paper empirically evaluates the performance gain resulting from adapting (or lifting) an existing analysis technique specific to the Goal-oriented Requirement Language (GRL) to a family of GRL models, all at once using a union model, compared to analyzing individual models. Our experiments show, based on the use of the IBM CPLEX optimizer, the usefulness and performance gains of using union models to perform a computationally expensive analysis, namely quantitative backward propagation, on families of GRL models.