Model transformation systems often contain transformation rules that are substantially similar to each other, causing maintenance issues and performance bottlenecks. To address these issues, we introduce variability-based model transformation . The key idea is to encode a set of similar rules into a compact representation, called variability-based rule . We provide an algorithm for applying such rules in an efficient manner. In addition, we introduce rule merging, a three-component mechanism for enabling the automatic creation of variability-based rules. Our rule application and merging mechanisms are supported by a novel formal framework, using category theory to provide precise definitions and to prove correctness. In two realistic application scenarios, the created variability-based rules enabled considerable speedups, while also allowing the overall specifications to become more compact.

中文翻译：

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基于变率的模型转换：形式化基础与应用

模型转换系统通常包含彼此基本相似的转换规则，从而导致维护问题和性能瓶颈。为了解决这些问题，我们引入基于可变性的模型转换. 关键思想是将一组相似的规则编码成一个紧凑的表示，称为基于可变性的规则. 我们提供了一种算法，可以有效地应用这些规则。此外，我们引入了规则合并，这是一种用于自动创建基于可变性的规则的三组件机制。我们的规则应用和合并机制由一个新颖的形式框架支持，使用类别理论来提供精确的定义并证明正确性。在两个实际的应用场景中，创建的基于可变性的规则实现了相当大的加速，同时还允许整体规范变得更加紧凑。