Software is a complex entity, and its development needs careful planning and a high amount of time and cost. To assess quality of program, software measures are very helpful. Amongst the existing measures, coupling is an important design measure, which computes the degree of interdependence among the entities of a software system. Higher coupling leads to cognitive complexity and thus a higher probability occurrence of faults. Well in time prediction of fault-prone modules assists in saving time and cost of testing. This paper aims to capture important aspects of coupling and then assess the effectiveness of these aspects in determining fault-prone entities in the software system. We propose two coupling metrics, i.e., Vovel-in and Vovel-out, that capture the level of coupling and the volume of information flow. We empirically evaluate the effectiveness of the Vovel metrics in determining the fault-prone classes using five projects, i.e., Eclipse JDT, Equinox framework, Apache Lucene, Mylyn, and Eclipse PDE UI. Model building is done using univariate logistic regression and later Spearman correlation coefficient is computed with the existing coupling metrics to assess the coverage of unique information. Finally, the least correlated metrics are used for building multivariate logistic regression with and without the use of Vovel metrics, to assess the effectiveness of Vovel metrics. The results show the proposed metrics significantly improve the predicting of fault prone classes. Moreover, the proposed metrics cover a significant amount of unique information which is not covered by the existing well-known coupling metrics, i.e., CBO, RFC, Fan-in, and Fan-out. This paper, empirically evaluates the impact of coupling metrics, and more specifically the importance of level and volume of coupling in software fault prediction. The results advocate the prudent addition of proposed metrics due to their unique information coverage and significant predictive ability.

中文翻译：

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Vovel 度量——用于改进软件故障预测的新型耦合度量

软件是一个复杂的实体，它的开发需要仔细的规划和大量的时间和成本。为了评估程序的质量，软件测量是非常有帮助的。在现有的度量中，耦合是一个重要的设计度量，它计算软件系统实体之间的相互依赖程度。更高的耦合导致认知复杂性，从而导致更高的故障发生概率。及时预测容易出错的模块有助于节省测试时间和成本。本文旨在捕捉耦合的重要方面，然后评估这些方面在确定软件系统中易发生故障的实体方面的有效性。我们提出了两个耦合度量，即 Vovel-in 和 Vovel-out，它们捕获耦合水平和信息流量。我们使用五个项目，即 Eclipse JDT、Equinox 框架、Apache Lucene、Mylyn 和 Eclipse PDE UI，凭经验评估 Vovel 指标在确定易出错类方面的有效性。模型构建是使用单变量逻辑回归完成的，然后使用现有的耦合度量计算 Spearman 相关系数，以评估独特信息的覆盖范围。最后，在使用和不使用 Vovel 指标的情况下，相关性最低的指标用于构建多元逻辑回归，以评估 Vovel 指标的有效性。结果表明，所提出的指标显着提高了对易出错类别的预测。此外，所提出的指标涵盖了大量独特的信息，这些信息未被现有的众所周知的耦合指标，即 CBO、RFC、Fan-in、和扇出。本文根据经验评估了耦合度量的影响，更具体地说是耦合级别和数量在软件故障预测中的重要性。由于其独特的信息覆盖范围和显着的预测能力，结果提倡谨慎添加建议的指标。