›› 2014,Vol. 29 ›› Issue (4): 724-736.doi: 10.1007/s11390-014-1462-5

• Special Section on Selected Paper from NPC 2011 • 上一篇    

特征模型中不一致性的交互式修复

Bo Wang1,2 (王波), Ying-Fei Xiong1,2 (熊英飞), Member, CCF, ACM, IEEE, Zhen-Jiang Hu1,2,3,* (胡振江), Member, ACM, IEEE, Hai-Yan Zhao1,2 (赵海燕), Member, CCF, ACM, IEEE, Wei Zhang1,2 (张伟), and Hong Mei1,2,4 (梅宏), Fellow, CCF, Member, ACM, IEEE   

  1. 1. Key Laboratory of High Confidence Software Technologies, Ministry of Education, Beijing 100871, China;
    2. Institute of Software, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China;
    3. National Institute of Informatics, Tokyo 101-8430, Japan;
    4. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
  • 出版日期:2014-07-05 发布日期:2014-07-05
  • 作者简介:Bo Wang received his B.S. degree in computer science from University of Electronic Science and Technology of China, Chengdu, in 2007, and his Ph.D. degree in computer science from Peking University, Beijing, in 2014. His research interests are requirements engineering, domain engineering, and variability model configuration.
  • 基金资助:

    This work is supported by the National High Technology Research and Development 863 Program of China under Grant No. 2013AA01A605, the National Basic Research 973 Program of China under Grant No. 2011CB302604, the National Natural Science Foundation of China under Grant Nos. 61121063, U1201252, 61272163, 61202071, and 60528006, and the Japan MEXT Grant-in-Aid for Scientific Research (A) under Grant No. 25240009.

Interactive Inconsistency Fixing in Feature Modeling

Bo Wang1,2 (王波), Ying-Fei Xiong1,2 (熊英飞), Member, CCF, ACM, IEEE, Zhen-Jiang Hu1,2,3,* (胡振江), Member, ACM, IEEE, Hai-Yan Zhao1,2 (赵海燕), Member, CCF, ACM, IEEE, Wei Zhang1,2 (张伟), and Hong Mei1,2,4 (梅宏), Fellow, CCF, Member, ACM, IEEE   

  1. 1. Key Laboratory of High Confidence Software Technologies, Ministry of Education, Beijing 100871, China;
    2. Institute of Software, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China;
    3. National Institute of Informatics, Tokyo 101-8430, Japan;
    4. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
  • Online:2014-07-05 Published:2014-07-05
  • About author:Bo Wang received his B.S. degree in computer science from University of Electronic Science and Technology of China, Chengdu, in 2007, and his Ph.D. degree in computer science from Peking University, Beijing, in 2014. His research interests are requirements engineering, domain engineering, and variability model configuration.
  • Supported by:

    This work is supported by the National High Technology Research and Development 863 Program of China under Grant No. 2013AA01A605, the National Basic Research 973 Program of China under Grant No. 2011CB302604, the National Natural Science Foundation of China under Grant Nos. 61121063, U1201252, 61272163, 61202071, and 60528006, and the Japan MEXT Grant-in-Aid for Scientific Research (A) under Grant No. 25240009.

特征模型被广泛应用于复用特定领域中一组相似产品的需求。在构造特征模型的过程中,一项基本的任务就是保证特征模型的一致性。这一任务包含检测及修复特征模型的不一致。目前,大部分现有方法均关注于不一致性的检测,忽略了不一致性的修复。本文提出一个新颖的基于动态优先级的交互式修复方法来修复特征模型中不一致性,并实现了相应的工具。该方法不但能自动推荐修复不一致性的解决方案,并通过动态修改优先级,帮助领域分析员逐步找到理想的解决方案。本文的关键贡献是:第一个将约束层次理论引入特征建模,采用优先级表达领域分析员对于约束的信心程度,并通过删除一个或多个低优先级的约束来解决冲突。本文通过两个实例研究展示了方法的可用性及可伸缩性(高效性)。

Abstract: Feature models have been widely adopted to reuse the requirements of a set of similar products in a domain. In feature models' construction, one basic task is to ensure the consistency of feature models, which often involves detecting and fixing of inconsistencies in feature models. While many approaches have been proposed, most of them focus on detecting inconsistencies rather than fixing inconsistencies. In this paper, we propose a novel dynamic-priority based approach to interactively fixing inconsistencies in feature models, and report an implementation of a system that not only automatically recommends a solution to fixing inconsistencies but also supports domain analysts to gradually reach the desirable solution by dynamically adjusting priorities of constraints. The key technical contribution is, as far as we are aware, the first application of the constraint hierarchy theory to feature modeling, where the degree of domain analysts' confidence on constraints is expressed by using priority and inconsistencies are resolved by deleting one or more lower-priority constraints. Two case studies demonstrate the usability and scalability (efficiency) of our new approach.

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