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Transformed k-nearest neighborhood output distance minimization for predicting the Defect Density of Software Projects
Journal of Systems and Software ( IF 3.5 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.jss.2020.110592
Cuauhtémoc López-Martín , Yenny Villuendas-Rey , Mohammad Azzeh , Ali Bou Nassif , Shadi Banitaan

Abstract Background Software defect prediction is one of the most important research topics in software engineering. An important product measure to determine the effectiveness of software processes is the defect density (DD). Cased-based reasoning (CBR) has been the prediction technique most widely applied in the software prediction field. The CBR involves k-nearest neighborhood for finding the number (k) of similar software projects selected to be involved in the prediction process. Objective To propose the application of a transformed k-nearest neighborhood output distance minimization (TkDM) algorithm to predict the DD of software projects to compare its prediction accuracy with those obtained from statistical regression, support vector regression, and neural networks. Method Data sets were obtained from the ISBSG release 2018. A leave-one-out cross validation method was performed. Absolute residual was used as the prediction accuracy criterion for models. Results Statistical significance tests among models showed that the TkDM had the best prediction accuracy than those ones from statistical regression, support vector regression, and neural networks. Conclusions A TkDM can be used for predicting the DD of new and enhanced software projects developed and coded in specific platforms and programming languages types.

中文翻译:

用于预测软件项目缺陷密度的变换 k 最近邻域输出距离最小化

摘要 背景软件缺陷预测是软件工程中最重要的研究课题之一。确定软件过程有效性的一个重要的产品度量是缺陷密度(DD)。基于案例的推理(CBR)一直是软件预测领域应用最广泛的预测技术。CBR 涉及 k 最近邻,用于查找选择参与预测过程的类似软件项目的数量 (k)。目的 提出应用变换的 k-最近邻域输出距离最小化 (TkDM) 算法来预测软件项目的 DD,以将其预测精度与从统计回归、支持向量回归和神经网络获得的预测精度进行比较。方法 数据集来自 ISBSG 2018 版。执行留一法交叉验证方法。绝对残差被用作模型的预测精度标准。结果 模型间的统计显着性检验表明,TkDM 的预测精度优于统计回归、支持向量回归和神经网络的预测精度。结论 TkDM 可用于预测在特定平台和编程语言类型中开发和编码的新的和增强的软件项目的 DD。
更新日期:2020-09-01
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