A Hybrid Set of Complexity Metrics for Large-Scale Object-Oriented Software Systems
A Hybrid Set of Complexity Metrics for Large-Scale Object-Oriented Software Systems
复制标题
大规模面向对象软件系统的复杂性度量的混合集
DOI：
10.1007/s11390-010-9398-x
复制
发表时间：
2010-11-01
影响因子：
1.9
通讯作者：
Zhou, Xiao-Yan
中科院分区：
文献类型：
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作者：
Ma, Yu-Tao;He, Ke-Qing;Zhou, Xiao-Yan
最近发现，大规模面向对象的（OO）软件系统具有共享全球网络特征，例如《小世界》和《自由规模》，它们超出了传统软件测量和评估方法的范围，可以测量各种粒度的复杂性，即粒度的复杂性，即图形，类（和对象）和源代码，就耦合和内聚力而言，我们提出了一组层次的指标，这是软件的最重要特征，并分析了12个样本12开源OO软件系统以经验验证跨级指标之间相关性的设定实验结果表明，从网络思维的角度来看，我们集合的图形测量值很好地补充了传统软件指标，并提供了有关易于缺陷的信息的更有效的信息实践中的课程
Large-scale object-oriented (OO) software systems have recently been found to share global network characteristics such assmall worldandscale free, which go beyond the scope of traditional software measurement and assessment methodologies. To measure the complexity at various levels of granularity, namely graph, class (and object) and source code, we propose a hierarchical set of metrics in terms of coupling and cohesion — the most important characteristics of software, and analyze a sample of 12 open-source OO software systems to empirically validate the set. Experimental results of the correlations between cross-level metrics indicate that the graph measures of our set complement traditional software metrics well from the viewpoint ofnetwork thinking, and provide more effective information about fault-prone classes in practice.