在软件产品线中开发领域功能需求的模型驱动方法

Jianmei Guo, Yinglin Wang, Zheying Zhang, J. Nummenmaa, Nan Niu
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引用次数: 9

摘要

现有的产品需求为软件产品线(SPLs)中的领域需求分析提供了丰富的资源。大多数现有的领域分析技术依赖于领域专家的经验和手工操作来识别产品需求的共性和可变性。它们通常需要高水平的手工工作和大量的前期投资,这可能会给SPL的采用带来令人望而却步的障碍。本研究提出了一种模型驱动的方法,从产品功能需求(PFRs)中半自动地导出领域功能需求(DFRs)。基于一个领域面向行动的关注点的语言特征,作者将Fillmore的语义框架应用于功能需求,并定义了PFRs和DFRs的元模型。将现有产品的功能需求构建为相应的PFR模型。根据提出的合并和细化规则,作者通过合并相同或相似的PFR并分析它们的共性和可变性,实现了从PFR模型到DFR模型的自动化转换。得到的DFR模型可以作为SPL的初始基础。作者以家庭安全系统(HSS) SPL为例,对本文的方法进行了论证,并给出了初步评价。作者的方法为dfr开发提供了严格的基于模型的支持,并以更少的时间和精力补充了现有的领域分析技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Model-driven approach to developing domain functional requirements in software product lines
Existing product requirements form a rich source for domain requirements analysis in software product lines (SPLs). Most existing domain analysis techniques depend on domain experts’ experience and manual operation to identify the commonalities and variabilities of product requirements. They often demand a high level of manual effort and a large up-front investment, which can present a prohibitive barrier for SPL adoption. This study proposes a model-driven approach to semi-automatically derive domain functional requirements (DFRs) from product functional requirements (PFRs). Based on the linguistic characterisation of a domain's action-oriented concerns, the authors apply Fillmore's semantic framework to functional requirements and define metamodels for PFRs and DFRs. Functional requirements of existing products are constructed as corresponding PFR models. Following the proposed merging and refinement rules, the authors approach automates the transformation from PFR models into DFR models by merging the same or similar PFRs and analysing their commonality and variability. The resulting DFR models can serve as an initial basis of the SPL. The authors demonstrate the authors approach using an example of a home security system (HSS) SPL and give a preliminary evaluation. The authors approach provides a rigorous model-based support for DFRs development and complements existing domain analysis techniques with less time and effort.
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