Different strokes for different folks: a case study on software metrics for different defect categories

Ayse Tosun Misirli, Bora Caglayan, A. Miranskyy, A. Bener, Nuzio Ruffolo
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引用次数: 19

Abstract

Defect prediction has been evolved with variety of metric sets, and defect types. Researchers found code, churn, and network metrics as significant indicators of defects. However, all metric sets may not be informative for all defect categories such that only one metric type may represent majority of a defect category. Our previous study showed that defect category sensitive prediction models are more successful than general models, since each category has different characteristics in terms of metrics. We extend our previous work, and propose specialized prediction models using churn, code, and network metrics with respect to three defect categories. Results show that churn metrics are the best for predicting all defects. The strength of correlation for code and network metrics varies with defect category: Network metrics have higher correlations than code metrics for defects reported during functional testing and in the field, and vice versa for defects reported during system testing.
不同的人有不同的方法:一个针对不同缺陷类别的软件度量的案例研究
缺陷预测已经随着各种度量集和缺陷类型而发展。研究人员发现代码、流失率和网络度量是缺陷的重要指示器。然而,所有的度量集可能不是为所有的缺陷类别提供信息,这样只有一个度量类型可以代表缺陷类别的大部分。我们之前的研究表明,缺陷类别敏感的预测模型比一般模型更成功,因为每个类别在度量方面具有不同的特征。我们扩展了之前的工作,并针对三个缺陷类别,使用流失、代码和网络度量提出了专门的预测模型。结果表明,流失度量是预测所有缺陷的最佳方法。代码和网络度量的相关性强度随缺陷类别的不同而变化:对于在功能测试和领域中报告的缺陷,网络度量比代码度量具有更高的相关性,对于在系统测试中报告的缺陷,反之亦然。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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