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CLC number: TP311

On-line Access: 2018-01-11

Received: 2016-06-11

Revision Accepted: 2016-09-14

Crosschecked: 2017-11-26

Cited: 0

Clicked: 2612

Citations:  Bibtex RefMan EndNote GB/T7714


Shu-juan Jiang


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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.11 P.1744-1753


A feature selection approach based on a similarity measure for software defect prediction

Author(s):  Qiao Yu, Shu-juan Jiang, Rong-cun Wang, Hong-yang Wang

Affiliation(s):  School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China; more

Corresponding email(s):   yuqiao@cumt.edu.cn, shjjiang@cumt.edu.cn

Key Words:  Software defect prediction, Feature selection, Similarity measure, Feature weights, Feature ranking list

Qiao Yu, Shu-juan Jiang, Rong-cun Wang, Hong-yang Wang. A feature selection approach based on a similarity measure for software defect prediction[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(11): 1744-1753.

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DOI - 10.1631/FITEE.1601322

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.


概要:软件缺陷预测旨在通过历史数据和能反映软件模块特性的软件特征来发现潜在缺陷。然而,有的特征可能与类别(有缺陷或无缺陷)的相关性较高,有的特征可能是冗余的或无关的。针对软件缺陷预测中不同特征与类别的相关性差异,本文提出一种基于相似性度量(similarity measure, SM)的特征选择方法。首先,根据不同类样本间的相似性来更新特征权重;然后,按照特征权重值降序排列生成特征排序列表,并依次选取特征排序列表中的所有特征子集;最后,在KNN(k-nearest neighbor)模型上验证所有特征子集的分类性能,并采用AUC(areaunder curve)指标进行度量。在11个美国航空航天局(NASA)数据集上进行实验验证,结果表明,与其它四种特征选择方法相比,本文方法具有与之相当甚至更高的分类性能。


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