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Test Data Generation for Model Transformations Combining Partition and Constraint Analysis

机译:用于模型变换的测试数据生成组合分区和约束分析

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Model-Driven Engineering (MDE) is a software engineering paradigm where models play a key role. In a MDE-based development process, models are successively transformed into other models and eventually into the final source code by means of a chain of model transformations. Since writing model transformations is an error-prone task, mechanisms to ensure their reliability are greatly needed. One way of achieving this is by means of testing. A challenging aspect when testing model transformations is the generation of adequate input test data. Most existing approaches generate test data following a black-box approach based on some sort of partition analysis that exploits the structural features of the source metamodel of the transformation. However, these analyses pay no attention to the OCL invariants of the metamodel or do it very superficially. In this paper, we propose a mechanism that systematically analyzes OCL constraints in the source metamodel in order to fine-tune this partition analysis and therefore, the generation of input test data. Our mechanism can be used in isolation, or combined with other black-box or white-box test generation approaches.
机译:模型驱动工程(MDE)是一种软件工程范例,模型播放关键作用。在基于MDE的开发过程中,模型连续转换为其他模型,并最终通过模型转换链进入最终源代码。由于编写模型转换是一种易于易于的任务,因此需要提供确保其可靠性的机制。实现这一目标的一种方法是通过测试。测试模型转换时的具有挑战性的方面是产生足够的输入测试数据。基于某些分区分析的黑盒方法后,大多数现有方法会在黑盒方法后生成测试数据,该分析利用转换源元模型的结构特征。然而,这些分析不关注元模型的OCL不变,或者非常过分。在本文中,我们提出了一种系统地分析源元模型中的OCL约束,以便微调该分区分析,因此,输入测试数据的产生。我们的机制可用于隔离,或与其他黑盒或白盒试验的方法相结合。

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