Software defect prediction is aimed to find potential defects based on historical data and software features. Software features can reflect the characteristics of software modules. However, some of these features may ...Software defect prediction is aimed to find potential defects based on historical data and software features. Software features can reflect the characteristics of software modules. However, some of these features may be more relevant to the class （defective or non-defective）, but others may be redundant or irrelevant. To fully measure the correlation between different features and the class, we present a feature selection approach based on a similarity measure （SM） for software defect prediction. First, the feature weights are updated according to the similarity of samples in different classes. Second, a feature ranking list is generated by sorting the feature weights in descending order, and all feature subsets are selected from the feature ranking list in sequence. Finally, all feature subsets are evaluated on a k-nearest neighbor （KNN） model and measured by an area under curve （AUC） metric for classification performance. The experiments are conducted on 11 National Aeronautics and Space Administration （NASA） datasets, and the results show that our approach performs better than or is comparable to the compared feature selection approaches in terms of classification performance.展开更多
基金Project supported by the National Natural Science Foundation of China （Nos. 61673384 and 61502497）, the Guangxi Key Laboratory of Trusted Software （No. kx201530）, the China Postdoctoral Science Foundation （No. 2015M581887）, and the Scientific Research Innovation Project for Graduate Students of Jiangsu Province, China （No. KYLX15 1443）
文摘Software defect prediction is aimed to find potential defects based on historical data and software features. Software features can reflect the characteristics of software modules. However, some of these features may be more relevant to the class （defective or non-defective）, but others may be redundant or irrelevant. To fully measure the correlation between different features and the class, we present a feature selection approach based on a similarity measure （SM） for software defect prediction. First, the feature weights are updated according to the similarity of samples in different classes. Second, a feature ranking list is generated by sorting the feature weights in descending order, and all feature subsets are selected from the feature ranking list in sequence. Finally, all feature subsets are evaluated on a k-nearest neighbor （KNN） model and measured by an area under curve （AUC） metric for classification performance. The experiments are conducted on 11 National Aeronautics and Space Administration （NASA） datasets, and the results show that our approach performs better than or is comparable to the compared feature selection approaches in terms of classification performance.
