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Improving Product Line Architecture Design and Customization by Raising the Level of Variability Modeling

  • Jiayi Zhu
  • Xin Peng
  • Stan Jarzabek
  • Zhenchang Xing
  • Yinxing Xue
  • Wenyun Zhao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6727)

Abstract

Product Line Architecture (PLA) plays a central role in software product line development. In order to support architecture-level variability modeling, most architecture description languages (ADLs) introduce architectural variation elements, such as optional component, connector and interface, which must be customized during product derivation. Variation elements are many, and design and customization of PLA at the level of individual variation elements are difficult and error-prone. We observed that developers usually perceive architecture variability from the perspective of variant features or variant design decisions that are mapped into groups of architecture variation elements. In the paper, we describe heuristics to identify configurations of variation elements that typically form such groups. We call them variation constructs. We developed an architecture variability management method and a tool that allow developers to work at the variation construct level rather than at the level of individual variation elements. We have applied and evaluated the proposed method in the development and maintenance of a medium-size financial product line. Our experience indicates that by raising variability modeling from variation element to construct level, architecture design and customizations become more intuitive. Not only does our method reduce the design and customization effort, but also better ensures consistent configuration of architectural variation elements, avoiding errors.

Keywords

software product line architecture variability ADL 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jiayi Zhu
    • 1
    • 2
  • Xin Peng
    • 1
    • 2
  • Stan Jarzabek
    • 3
  • Zhenchang Xing
    • 3
  • Yinxing Xue
    • 3
  • Wenyun Zhao
    • 1
    • 2
  1. 1.Shanghai Key Laboratory of Intelligent Information ProcessingChina
  2. 2.School of Computer ScienceFudan UniversityChina
  3. 3.School of ComputingNational University of SingaporeSingapore

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