Clustering (modularisation) techniques are often employed for the meaningful decomposition of a program aiming to understand it. In the software clustering context, several external metrics are presented to evaluate and validate the resultant clustering obtained by an algorithm. These metrics use a ground-truth decomposition to evaluate a resultant clustering. When there exists no ground-truth decomposition for a software system, internal metrics are utilised to validate clustering algorithms. Due to the comparison with a reference decomposition, external metrics are preferred to internal metrics. Available internal metrics used to measure the clustering quality are not appropriate for evaluating because they do not consider the purpose of software clustering, which is to understand a software system. In this study, the authors present six criteria that influence the understanding of a program. Then the authors design an internal metric for estimating the software clustering quality considering those criteria. They selected ten folders of Mozilla Firefox with different sizes and functionalities to assess the reliability of the proposed metric. The experimental results confirm that the proposed internal metric is more accurate than the existing internal metrics in terms of proximity to expert decomposition. The proposed internal metric can be a substitute for external metrics.

中文翻译：

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软件聚类算法有效性的新内部指标

聚类（模块化）技术通常用于有意义地分解旨在理解该程序的程序。在软件聚类环境中，提出了几种外部度量来评估和验证通过算法获得的结果聚类。这些度量使用真实的分解来评估结果聚类。当软件系统不存在地面真相分解时，将使用内部指标来验证聚类算法。由于与参考分解的比较，外部指标优于内部指标。用于衡量集群质量的可用内部指标不适合评估，因为它们没有考虑软件集群的目的，即了解软件系统。在这个研究中，作者提出了六个影响程序理解的标准。然后，作者考虑这些标准，设计了一个内部指标来估算软件集群质量。他们选择了十个Mozilla Firefox文件夹，它们具有不同的大小和功能，以评估所提出指标的可靠性。实验结果证实，就专家分解的接近性而言，所提出的内部度量比现有内部度量更准确。建议的内部指标可以替代外部指标。他们选择了十个Mozilla Firefox文件夹，它们具有不同的大小和功能，以评估所提出指标的可靠性。实验结果证实，在接近专家分解方面，所提出的内部度量比现有内部度量更准确。建议的内部指标可以替代外部指标。他们选择了十个Mozilla Firefox文件夹，它们具有不同的大小和功能，以评估所提出指标的可靠性。实验结果证实，就专家分解的接近性而言，所提出的内部度量比现有内部度量更准确。建议的内部指标可以替代外部指标。