Model management techniques help tame the complexity caused by the many models used in large-scale software development; however, these techniques have focused on operators to manipulate individual models rather than entire collections of them. In this work, we begin to address this gap by adapting the widely used map, reduce and filter collection operators for collections of models represented by megamodels. Key parts of this adaptation include the special handling of relationships between models and the use of polymorphism to support heterogeneous model collections. We evaluate the complexity of our operators analytically and demonstrate their applicability on six diverse megamodel management scenarios. We describe our tool support for the approach and evaluate its scalability experimentally as well as its applicability on a practical application from the automotive domain.

中文翻译：

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使用集合运算符的异构宏模型管理

模型管理技术有助于解决由大规模软件开发中使用的许多模型引起的复杂性；但是，这些技术集中于操作员来操纵单个模型而不是模型的整个集合。在这项工作中，我们开始通过调整广泛使用的map，reduce和filter来解决这一差距。集合运算符，用于以大型模型表示的模型的集合。这种改编的关键部分包括对模型之间关系的特殊处理以及使用多态性来支持异构模型集合。我们通过分析评估运营商的复杂性，并证明其在六个不同的巨型模型管理方案中的适用性。我们描述了我们对该方法的工具支持，并通过实验评估了其可扩展性以及其在汽车领域实际应用中的适用性。