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Configurable three‐way model merging
Software: Practice and Experience ( IF 3.5 ) Pub Date : 2020-05-10 , DOI: 10.1002/spe.2835
Mohammadreza Sharbaf 1 , Bahman Zamani 1
Affiliation  

Software development is a collaborative activity that requires teams of software engineers to cooperate and work in parallel on versions of models. However, model management techniques such as model differencing, merging, and versioning have turned out to be difficult challenges, due to the complexity of operations and graph‐like nature of models. Therefore, a well‐developed support for model merging process, as well as conflict management, is highly desired. This paper presents a novel process for model merging, called the Epsilon‐based Three‐way Merging Process (E3MP) process. Model merging is a significant problem where there are different versions of a system model amongst modeler teams. E3MP includes three components implemented into the Epsilon framework. First, modelers can define domain‐specific rules that customize the merging process. Second, E3MP enables an automated method for syntactic and semantic conflict detection amongst different versions of the system model. Third, E3MP puts forward a pattern‐based approach for conflict resolution. We applied two generic benchmarks to assess conflict detection and resolution capabilities of our approach and carried out an initial scalability evaluation for the model merge with large models and large change sets. The results of our experiments revealed that the proposed process allows generating consistent and semantically correct merged models.

中文翻译:

可配置的三路模型合并

软件开发是一项协作活动,需要软件工程师团队协作并在模型版本上并行工作。然而,由于操作的复杂性和模型的类图性质,模型差异、合并和版本控制等模型管理技术已被证明是困难的挑战。因此,非常需要对模型合并过程以及冲突管理的完善支持。本文提出了一种新的模型合并过程,称为基于 Epsilon 的三路合并过程(E3MP)过程。当建模团队之间存在不同版本的系统模型时,模型合并是一个重要问题。E3MP 包括在 Epsilon 框架中实现的三个组件。首先,建模者可以定义自定义合并过程的特定领域规则。其次,E3MP 启用了一种自动化方法,用于在系统模型的不同版本之间进行句法和语义冲突检测。第三,E3MP 提出了一种基于模式的冲突解决方法。我们应用了两个通用基准来评估我们方法的冲突检测和解决能力,并对模型与大型模型和大型变更集的合并进行了初始可扩展性评估。我们的实验结果表明,所提出的过程允许生成一致且语义正确的合并模型。我们应用了两个通用基准来评估我们方法的冲突检测和解决能力,并对模型与大型模型和大型变更集的合并进行了初始可扩展性评估。我们的实验结果表明,所提出的过程允许生成一致且语义正确的合并模型。我们应用了两个通用基准来评估我们方法的冲突检测和解决能力,并对模型与大型模型和大型变更集的合并进行了初始可扩展性评估。我们的实验结果表明,所提出的过程允许生成一致且语义正确的合并模型。
更新日期:2020-05-10
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