避免不必要的信息丢失：基于三重图文法的正确高效的模型同步
Avoiding Unnecessary Information Loss: Correct and Efficient Model Synchronization Based on Triple Graph Grammars
摘要：
模型同步，即，在模型改变之后恢复两个相关模型之间的一致性的任务是具有挑战性的任务。三重图文法（TGG）通过描述如何创建一致的模型对的规则来指定模型一致性。这些规则可用于自动导出进一步的规则，这些规则描述如何将更改从一个模型传播到另一个模型，或者如何以保证传播是可能的方式更改一个模型。但是，将模型同步限制为这些派生规则可能会导致在更改传播期间不必要地删除和重新创建模型元素。这是低效的并且可能导致不必要的信息丢失，即，当删除的元素包含第二模型中没有表示的信息时，该信息不能被容易地恢复。最近开发了捷径规则，通过重用现有的模型元素来避免不必要的信息丢失。在本文中，我们展示了如何自动获得（捷径）修复规则的捷径规则传播的变化，从而避免信息丢失和模型同步加速。我们的基于规则的模型同步过程的关键成分是这些修复规则和增量模式匹配器通知他们的合适的应用程序。我们证明了这个同步过程的终止性和正确性，并讨论了它的完备性。作为概念证明，我们已经在eMotivation中实现了此同步过程，eMotivation是一种最先进的模型转换工具，具有固有的双向性支持。我们的评估表明，修复过程的基础上（捷径）修复规则有相当大的减少信息丢失和提高性能相比，以前的模型同步过程的基础上TGG。
英文摘要：
Model synchronization, i.e., the task of restoring consistency between two interrelated models after a model change, is a challenging task. Triple Graph Grammars (TGGs) specify model consistency by means of rules that describe how to create consistent pairs of models. These rules can be used to automatically derive further rules, which describe how to propagate changes from one model to the other or how to change one model in such a way that propagation is guaranteed to be possible. Restricting model synchronization to these derived rules, however, may lead to unnecessary deletion and recreation of model elements during change propagation. This is inefficient and may cause unnecessary information loss, i.e., when deleted elements contain information that is not represented in the second model, this information cannot be recovered easily. Short-cut rules have recently been developed to avoid unnecessary information loss by reusing existing model elements. In this paper, we show how to automatically derive (short-cut) repair rules from short-cut rules to propagate changes such that information loss is avoided and model synchronization is accelerated. The key ingredients of our rule-based model synchronization process are these repair rules and an incremental pattern matcher informing about suitable applications of them. We prove the termination and the correctness of this synchronization process and discuss its completeness. As a proof of concept, we have implemented this synchronization process in eMoflon, a state-of-the-art model transformation tool with inherent support of bidirectionality. Our evaluation shows that repair processes based on (short-cut) repair rules have considerably decreased information loss and improved performance compared to former model synchronization processes based on TGGs.
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