部分注释模型中可变性注释的确定

Sandra Greiner, B. Westfechtel
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引用次数: 8

摘要

在模型驱动软件产品线工程(simple)中,产品的超集是在模型之上开发的。特征模型通常描述软件的区别性和共性因素。在注释方法中,模型元素与可变性注释相关联,可变性注释是定义产品中元素可见的特征上的布尔表达式。当产品线在不同的模型上定义时,开发人员希望注释一种类型的模型,并将其转换为不同的表示,例如,将(UML)类图转换为关系数据库模式,以建立对象-关系映射。手动将注释分配给目标模型是一项容易出错且费力的任务。在黑盒方法中,我们自动将正确的注释分配给目标模型,而无需分析转换规范。在1:1映射的情况下,这是一项简单的任务,源元素的注释被复制到目标模型中的相应元素。通常这种类型的信息是可用的,例如,在转换执行期间写入的跟踪中。在现实中,更复杂的映射频繁出现,但其对应关系难以确定。假设目标模型已经使用1:1对应的注释进行了注释,那么仍有一定数量的元素没有注释。本文提出了在部分标注模型中确定缺失标注的策略。我们将全局包含策略与考虑模型结构的更复杂的策略进行比较。由于我们在一个模型上局部应用了缺失的注释,因此我们解决了一个通用的spe问题,即完全注释的模型减少了用户的手工工作,并且适合过滤。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On Determining Variability Annotations In Partially Annotated Models
In model-driven software product line engineering (SPLE) the superset of products is developed over models. A feature model typically states the discriminating and common factors of the software. In annotative approaches model elements are associated with variability annotations which are boolean expressions over the features defining in which products the elements are visible. When the product line is defined over different models, the developer wants to annotate the model of one type and transform it to different representations, e.g., a (UML) class diagram into a relational database schema for establishing an object-relational mapping. Assigning the annotations manually to the target model is an error-prone and laborious task. In a black-box approach we automatically assign the correct annotations to the target model without analyzing the transformation specification. It is an easy task in the case of 1:1 mappings where the annotation of the source element is copied to the corresponding element in the target model. Typically this kind of information is available, e.g., in traces written during the transformation execution. In reality, more complex mappings are frequent but the correspondences harder to determine. Assuming a target model is already annotated with the annotation of 1:1 correspondences, a certain number of elements remains without annotations. This paper contributes strategies to determine missing annotations in partially annotated models. We compare a global include strategy with more sophisticated ones which take the model structure into account. Since we apply missing annotations locally on one model, we solve a general SPLE problem where completely annotated models reduce the manual user effort and are desirable for filtering.
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