论文标题
使用支持向量机的软件缺陷预测
Software Defect Prediction Using Support Vector Machine
论文作者
论文摘要
软件缺陷预测是软件开发生命周期期间的重要任务，因为它可以帮助管理人员确定最缺陷的主持模块。因此，它可以降低测试成本并有效地分配测试资源。许多分类方法可用于确定软件是否有缺陷。支持向量机（SVM）尚未广泛用于此类问题，因为它在不同的数据集和参数设置上应用时不稳定。影响准确性的主要参数是内核函数的选择。在以前的论文中尚未对内核功能的使用进行彻底研究。因此，本研究通过六个不同的内核函数检查了SVM的性能和准确性。来自Promise项目的各种公共数据集从经验上验证了我们的假设。结果表明，没有内核函数可以在不同的实验设置上提供稳定的性能。此外，将PCA用作功能还原算法显示出一些数据集的精度提高。
Software defect prediction is an essential task during the software development Lifecycle as it can help managers to identify the most defect-proneness modules. Thus, it can reduce the test cost and assign testing resources efficiently. Many classification methods can be used to determine if the software is defective or not. Support Vector Machine (SVM) has not been used extensively for such problems because of its instability when applied on different datasets and parameter settings. The main parameter that influences the accuracy is the choice of the kernel function. The use of kernel functions has not been studied thoroughly in previous papers. Therefore, this research examines the performance and accuracy of SVM with six different kernel functions. Various public datasets from the PROMISE project empirically validate our hypothesis. The results demonstrate that no kernel function can give stable performance across different experimental settings. In addition, the use of PCA as a feature reduction algorithm shows slight accuracy improvement over some datasets.
