A Variability-Based Approach to Reusable and Efficient Model Transformations
A Variability-Based Approach to Reusable and Efficient Model Transformations
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基于可变性的可重用且高效的模型转换方法
DOI：
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发表时间：
2015
期刊：
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通讯作者：
G. Taentzer
中科院分区：
文献类型：
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作者：
D. Strüber;J. Rubin;M. Chechik;G. Taentzer
大型模型转换系统通常包含彼此基本相似的转换规则，这会导致规则不确定地应用的系统的性能瓶颈，只要其中一个规则是适用的。我们通过引入基于可变性的图变换来解决这个问题。我们正式定义基于变量的规则，并贡献一种新的匹配发现算法应用它们。我们证明了我们的方法的正确性，通过展示其等价于经典的单独应用规则，并展示了一个现实的转换场景上实现的性能加速。
Large model transformation systems often contain transformation rules that are substantially similar to each other, causing performance bottlenecks for systems in which rules are applied nondeterministically, as long as one of them is applicable. We tackle this problem by introducing variability-based graph transformations. We formally define variability-based rules and contribute a novel match-finding algorithm for applying them. We prove correctness of our approach by showing its equivalence to the classic one of applying the rules individually, and demonstrate the achieved performance speed-up on a realistic transformation scenario.