基于上下文的包内聚度量

Tianlin Zhou, Baowen Xu, Liang Shi, Yuming Zhou, Lin Chen
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引用次数: 15

摘要

软件包在理解、构建和维护大型软件系统方面起着至关重要的作用。内聚性作为一种重要的设计属性,可以用来预测包装的质量。尽管在过去十年中提出了许多包内聚度量,但它们主要集中在组件之间的包内数据依赖关系上,在许多情况下,这不足以表示包的语义。为了解决这个问题,我们提出了一个新的包内聚度量,称为SCC,假设两个组件具有相似的上下文,则它们紧密相关。与现有的工作相比,SCC使用两个组件的共同上下文来推断它们之间是否有密切的关系,这涉及到包间和包内的数据依赖。因此,它能够揭示组件之间的语义关系。我们通过案例研究证明了SCC的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Measuring Package Cohesion Based on Context
Packages play a critical role to understand, construct and maintain large-scale software systems. As an important design attribute, cohesion can be used to predict the quality of packages. Although a number of package cohesion metrics have been proposed in the last decade, they mainly converge on intra-package data dependences between components, which are inadequate to represent the semantics of packages in many cases. To address this problem, we propose a new cohesion metric for package called SCC on the assumption that two components are related tightly if they have similar contexts. Compared to existing works, SCC uses the common context of two components to infer whether they have close relation or not, which involves both inter- and intra- package data dependences. It is hence able to reveal semantic relations between components. We demonstrate the effectiveness of SCC by case studies.
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