Tool support for systematic class identification in object-oriented software architectures

K. S. Barber, T. Graser
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引用次数: 6

Abstract

Software architectures have received considerable attention in both research and practice for representing system stakeholder concerns, and many researchers have leveraged object oriented models and methods for software architecture representation and evaluation. While the benefits associated with object oriented approaches are closely aligned with desirable qualities for software architectures (e.g., reusability, extensibility, comprehensibility, performance), these benefits are only realized through rational decision making by the analyst when identifying object oriented classes from a requirements specification. Traditionally, the class identification process has been somewhat subjective, with different analysts often arriving at completely different class models from the same requirements specification. Since this subjectivity does not lend itself to automation, little tool support is available for identifying object oriented classes and their relationships. The paper describes a tool under development, Reference Architecture Representation Environment (RARE), designed to systematically guide the analyst through class identification by applying heuristics associated with quality attributes and evaluating the resulting architecture based on relevant static metrics. RARE helps address a number of challenges typically faced during the class identification process, including: (1) developing an architecture that reflects the quality attributes prioritized by the analyst, (2) managing inherent conflicts between selected attributes, and (3) capturing analyst expertise and rationale for use by others.
面向对象软件体系结构中系统类识别的工具支持
软件架构在表示系统涉众关注的研究和实践中都受到了相当大的关注,许多研究人员已经利用面向对象的模型和方法来表示和评估软件架构。虽然与面向对象方法相关的好处与软件体系结构的理想质量(例如,可重用性、可扩展性、可理解性、性能)密切相关,但这些好处只能通过分析人员从需求规范中识别面向对象类时的合理决策来实现。传统上,类识别过程有些主观,不同的分析人员经常从相同的需求规范中得出完全不同的类模型。由于这种主观性本身不适合自动化,因此很少有工具支持用于识别面向对象的类及其关系。本文描述了一个正在开发的工具,参考体系结构表示环境(RARE),旨在通过应用与质量属性相关的启发式方法和基于相关静态度量评估结果体系结构,系统地指导分析人员进行类识别。RARE有助于解决在类识别过程中通常面临的许多挑战,包括:(1)开发反映分析人员优先考虑的质量属性的体系结构,(2)管理所选属性之间的内在冲突,以及(3)获取分析人员的专业知识和其他人使用的基本原理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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