In model-driven methodologies, model matching is the process of finding a matching pair for every model element between two or more software models. Model matching is an important task as it is often used while differencing and merging models, which are key processes in version control systems. There are a number of different approaches to model matching, with most of them focusing on different goals, i.e., the accuracy of the matching process, or the generality of the algorithm. Moreover, there exist algorithms that use the textual representations of the models during the matching process. We present a systematic literature review that was carried out to obtain the state-of-the-art of model matching techniques. The search process was conducted based on a well-defined methodology. We have identified a total of 3274 non-duplicate studies, out of which 119 have been included as primary studies for this survey. We present the state-of-the-art of model matching, highlighting the differences between different matching techniques, mainly focusing on text-based and graph-based algorithms. Finally, the main open questions, challenges, and possible future directions in the field of model matching are discussed, also including topics like benchmarking, performance and scalability, and conflict handling.

中文翻译：

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系统评价模型驱动方法中使用的匹配技术

在模型驱动的方法中，模型匹配是为两个或多个软件模型之间的每个模型元素找到匹配对的过程。模型匹配是一项重要的任务，因为它通常在差异和合并模型时使用，这是版本控制系统中的关键过程。有多种不同的模型匹配方法，其中大多数着眼于不同的目标，即匹配过程的准确性或算法的通用性。而且，存在在匹配过程中使用模型的文本表示的算法。我们提供了系统的文献综述，旨在获得模型匹配技术的最新信息。搜索过程是基于定义明确的方法进行的。我们总共确定了3274个非重复研究，其中有119项作为本研究的基础研究。我们介绍了模型匹配的最新技术，重点介绍了不同匹配技术之间的差异，主要侧重于基于文本和基于图的算法。最后，讨论了模型匹配领域中主要的开放性问题，挑战和可能的未来方向，还包括基准测试，性能和可伸缩性以及冲突处理等主题。