A dynamic-priority based approach to fixing inconsistent feature models

  • Authors:

  • Bo Wang;Yingfei Xiong;Zhenjiang Hu;Haiyan Zhao;Wei Zhang;Hong Mei

  • Affiliations:

  • Key Laboratory of High Confidence Software Technologies, Ministry of Education, China and Institute of Software, School of EECS, Peking University, Beijing, China;Generative Software Development Lab, The University of Waterloo, Canada;GRACE Center, National Institute of Informatics, Japan;Key Laboratory of High Confidence Software Technologies, Ministry of Education, China and Institute of Software, School of EECS, Peking University, Beijing, China;Key Laboratory of High Confidence Software Technologies, Ministry of Education, China and Institute of Software, School of EECS, Peking University, Beijing, China;Key Laboratory of High Confidence Software Technologies, Ministry of Education, China and Institute of Software, School of EECS, Peking University, Beijing, China

  • Venue:
  • MODELS'10 Proceedings of the 13th international conference on Model driven engineering languages and systems: Part I
  • Year:
  • 2010

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Abstract

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. Several approaches have been proposed to detect inconsistencies, but few focus on the problem of fixing inconsistent feature models. In this paper, we propose a dynamic-priority based approach to fixing inconsistent feature models, with the purpose of helping domain analysts find solutions to inconsistencies efficiently. The basic idea of our approach is to first recommend a solution automatically, then gradually reach the desirable solution by dynamically adjusting priorities of constraints. To this end, we adopt the constraint hierarchy theory to express the degree of domain analysts' confidence on constraints (i.e. the priorities of constraints) and resolve inconsistencies among constraints. Two case studies have been conducted to demonstrate the usability and scalability of our approach.