首页> 外文会议>Reverse Engineering, 2001. Proceedings. Eighth Working Conference on >CRAFT: a framework for evaluating software clustering results in the absence of benchmark decompositions Clustering Results Analysis Framework and Tools
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CRAFT: a framework for evaluating software clustering results in the absence of benchmark decompositions Clustering Results Analysis Framework and Tools

机译:CRAFT:在没有基准分解的情况下评估软件集群结果的框架集群结果分析框架和工具

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Software clustering algorithms are used to create high-level views of a system's structure using source code-level artifacts. Software clustering is an active area of research that has produced many clustering algorithms. However, we have so far seen very little work that investigates how the results of these algorithms can be evaluated objectively in the absence of a benchmark decomposition or without the active participation of the original designers of the system. Ideally, for a given system, art agreed upon reference (benchmark) decomposition of the system's structure would exist, allowing the results of various clustering algorithms to be compared against it. Since such benchmarks seldom exist, we seek alternative methods to gain confidence in the quality of results produced by software clustering algorithms. In this paper, we present a tool that supports the evaluation of software clustering results in the absence of a benchmark decomposition.
机译:软件集群算法用于使用源代码级构件来创建系统结构的高级视图。软件聚类是一个活跃的研究领域,已经产生了许多聚类算法。但是,到目前为止,我们几乎没有工作来研究在没有基准分解或没有系统原始设计者的积极参与的情况下,如何客观地评估这些算法的结果。理想情况下,对于给定的系统,将存在对系统结构的参考(基准)分解达成共识的技术,从而可以将各种聚类算法的结果与之进行比较。由于这样的基准很少存在,因此我们寻求替代方法来增强对软件聚类算法产生的结果质量的信心。在本文中,我们提出了一种在没有基准分解的情况下支持软件聚类结果评估的工具。

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