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2017, Volume 18, Issue 11

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Frontiers of Information Technology & Electronic Engineering >> 2017, Volume 18, Issue 11 doi: 10.1631/FITEE.1601322

Afeature selection approach based on a similarity measure for software defect prediction

. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China.. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China.

Available online: 2018-03-08
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Abstract

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.

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