Tolerating Inconsistency in Feature Models
Tolerating Inconsistency in Feature Models
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发表时间：
2010
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通讯作者：
Bo Wang-;Zhenjiang Hu;Yingfei Xiong;Haiyan Zhao;Wei Zhang-;Hong Mei
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作者：
Bo Wang-;Zhenjiang Hu;Yingfei Xiong;Haiyan Zhao;Wei Zhang-;Hong Mei
特征模型已被广泛采用来重用领域中一组类似产品的需求。在构造特征模型时，总是要保证特征模型的一致性。因此，在特征模型的构建过程中，容忍不一致是很重要的。容忍不一致的通常方法是找到最小的不可满足核。然而，寻找最小不可满足核耗时较长，降低了其实用性。在本文中，我们提出了一种基于优先级的特征模型不一致性容错方法。我们方法的基本思想是找到较弱的未满足约束，同时保持其余特征模型的一致性。在构建特征模型时，我们的方法允许在基于优先级的操作的帮助下出现不一致。为此，我们采用约束层次理论来表示领域分析人员对约束的信任程度(即约束的优先级)，并容忍特征模型中的不一致。实验表明，我们的系统可以扩展到大型特征模型。
Feature models have been widely adopted to reuse the requirements of a set of similar products in a domain. When constructing feature models, it is dicult to always ensure the consistency of feature models. Therefore, tolerating inconsistencies is important during the construction of feature models. The usual way of tolerating inconsistencies is to nd the minimal unsatisable core. However, identifying the minimal unsatisable core is time-consuming, which decreases itself the practicability. In this paper, we propose a priority based approach to tolerating inconsistencies in feature models eciently. The basic idea of our approach is to nd the weaker unsatised constraints, while keeping the rest of the feature model consistent. Our approach tolerates inconsistencies with the help of priority based operations while building feature models. To this end, we adopt the constraint hierarchy theory to express the degree of domain analysts’ condence on constraints (i.e. the priorities of constraints) and tolerate inconsistencies in feature models. Experiments have been conducted to demonstrate that our system can scale up to large feature models.