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Inter-model Consistency Checking Using Triple Graph Grammars and Linear Optimization Techniques

机译:使用三图语法和线性优化技术的模型间一致性检查

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摘要

An important task in Model-Driven Engineering (MDE) is to check consistency between two concurrently developed yet related models. Practical approaches to consistency checking, however, are scarce in MDE. Triple Graph Grammars (TGGs) are a rule-based technique to describe the consistency of two models together with correspondences. While TGGs seem promising for consistency checking with their precise consistency notion and explicit traceability information, the substantial search space involved in determining the "optimal" set of rule applications in a consistency check has arguably prevented mature tool support so far. In this paper, we close this gap by combining TGGs with linear optimization techniques. We formulate decisions between single rule applications of a consistency check as integer inequalities, which serve as input for an optimization problem used to detect maximum consistent portions of two models. To demonstrate our approach, we provide an experimental evaluation of the tool support made feasible by this formalization.
机译:模型驱动工程(MDE)中的一项重要任务是检查两个同时开发但相关的模型之间的一致性。但是,在MDE中缺少实用的一致性检查方法。三重图文法(TGG)是一种基于规则的技术,用于描述两个模型以及对应关系的一致性。尽管TGG凭借其精确的一致性概念和明确的可追溯性信息看起来很有希望进行一致性检查,但到目前为止,在一致性检查中确定“最佳”规则应用程序集所涉及的大量搜索空间无疑可以阻止成熟的工具支持。在本文中,我们通过将TGG与线性优化技术相结合来弥合这一差距。我们将一致性检查的单规则应用之间的决策公式化为整数不等式,以作为用于检测两个模型的最大一致性部分的优化问题的输入。为了证明我们的方法,我们提供了对该形式化工具支持的实验评估。

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