An Empirical Exploration of the Distributions of the Chidamber and Kemerer Object-Oriented Metrics Suite
An Empirical Exploration of the Distributions of the Chidamber and Kemerer Object-Oriented Metrics Suite
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DOI：
10.1023/b:emse.0000048324.12188.a2
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发表时间：
2004
影响因子：
4.1
通讯作者：
G. Succi;W. Pedrycz;S. Djokic;P. Zuliani;B. Russo
中科院分区：
文献类型：
--
作者：
G. Succi;W. Pedrycz;S. Djokic;P. Zuliani;B. Russo
Chidamber和Kemerer （CK）提出的面向对象度量套件是一种改进面向对象设计和开发实践的度量方法。然而，现有的研究表明，一些指标与其他指标的低范围之间存在共线性的痕迹，这两个事实可能危及基于CK套件的模型的有效性。由于高相关性可能是共线性的一个指标，在本文中，我们经验地确定了CK指标之间高相关性和低范围的程度。为了得出尽可能多的一般性结论，我们从大型数据集（200个公共领域项目）中提取了CK指标，并应用统计荟萃分析技术来加强结果的有效性。在整个项目中，我们发现一些指标与其他指标的低范围之间存在中等（~ 0.50）到高相关性（>0.80）。该实证分析的结果为研究人员和从业人员提供了三个主要建议：a)避免在相关性超过0.80的CK指标的预测系统中使用；b)测试具有中等相关性（0.50和0.60之间）的指标的共线性；c)避免在呈现低方差的指标的连续参数回归分析中作为响应使用。因此，这可能表明一个预测系统可能不是基于整个CK指标套件，而只是基于那些不呈现高相关性或低范围的指标组成的子集。
The object-oriented metrics suite proposed by Chidamber and Kemerer (CK) is a measurement approach towards improved object-oriented design and development practices. However, existing studies evidence traces of collinearity between some of the metrics and low ranges of other metrics, two facts which may endanger the validity of models based on the CK suite. As high correlation may be an indicator of collinearity, in this paper, we empirically determine to what extent high correlations and low ranges might be expected among CK metrics.To draw as much general conclusions as possible, we extract the CK metrics from a large data set (200 public domain projects) and we apply statistical meta-analysis techniques to strengthen the validity of our results. Homogenously through the projects, we found a moderate (∼0.50) to high correlation (>0.80) between some of the metrics and low ranges of other metrics.Results of this empirical analysis supply researchers and practitioners with three main advises: a) to avoid the use in prediction systems of CK metrics that have correlation more than 0.80 b) to test for collinearity those metrics that present moderate correlations (between 0.50 and 0.60) c) to avoid the use as response in continuous parametric regression analysis of the metrics presenting low variance. This might therefore suggest that a prediction system may not be based on the whole CK metrics suite, but only on a subset consisting of those metrics that do not present either high correlation or low ranges.