The Impact of Feature Selection on Defect Prediction Performance: An Empirical Comparison
The Impact of Feature Selection on Defect Prediction Performance: An Empirical Comparison
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DOI：
10.1109/issre.2016.13
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发表时间：
2016-10
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影响因子：
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通讯作者：
Zhou Xu;Jin Liu;Zijiang Yang;G. An;Xiangyang Jia
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文献类型：
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作者：
Zhou Xu;Jin Liu;Zijiang Yang;G. An;Xiangyang Jia
软件缺陷预测旨在通过构建预测模型来确定软件模块是否容易出现缺陷。这种模型的性能容易受到数据集的高维的影响，这些数据集可能包含不相关和冗余的特征。特征选择是用来缓解这个问题的。由于已经提出了许多特征选择方法，因此有必要对这些方法进行分析和比较。先前的实证研究可能存在潜在的争议和局限性，例如结果相互矛盾，使用私人数据集和不适当的统计测试技术。这一观察结果促使我们进行仔细的实证研究，通过考虑几个潜在的偏差来源，如数据集中的噪声和数据集类型，来增强实验结论的可信度。在本文中，我们研究了32种特征选择方法对两个版本的NASA数据集（即有噪声和干净的NASA数据集）和一个开源的AEEEM数据集的缺陷预测性能的影响。我们使用最先进的双斯科特-诺特测试技术来分析这些方法。实验结果表明，这些特征选择方法对缺陷预测性能的有效性在所有数据集上都有显著差异。
Software defect prediction aims to determine whether a software module is defect-prone by constructing prediction models. The performance of such models is susceptible to the high dimensionality of the datasets that may include irrelevant and redundant features. Feature selection is applied to alleviate this issue. Because many feature selection methods have been proposed, there is an imperative need to analyze and compare these methods. Prior empirical studies may have potential controversies and limitations, such as the contradictory results, usage of private datasets and inappropriate statistical test techniques. This observation leads us to conduct a careful empirical study to reinforce the confidence of the experimental conclusions by considering several potential source of bias, such as the noise in the dataset and the dataset types. In this paper, we investigate the impact of 32 feature selection methods on the defect prediction performance over two versions of the NASA dataset (i.e., the noisy and clean NASA datasets) and one open source AEEEM dataset. We use a state-of-the-art double Scott-Knott test technique to analyze these methods. Experimental results show that the effectiveness of these feature selection methods on defect prediction performance varies significantly over all the datasets.