NDLI: FECAR: A Feature Selection Framework for Software Defect Prediction

Content Provider	IEEE Xplore Digital Library
Author	Shulong Liu Xiang Chen Wangshu Liu Jiaqiang Chen Qing Gu Daoxu Chen
Copyright Year	2014
Description	Author affiliation: State Key Lab. for Novel Software Technol., Nanjing Univ., Nanjing, China (Shulong Liu; Xiang Chen; Wangshu Liu; Jiaqiang Chen; Qing Gu; Daoxu Chen)
Abstract	Software defect prediction can classify new software entities into either buggy or clean. However the effectiveness of existing methods is influenced by irrelevant and redundant features. In this paper, we propose a new feature selection framework FECAR using Feature Clustering And feature Ranking. This framework firstly partitions original features into k clusters based on FF-Correlation measure. Then it selects relevant features from each cluster based on FC-Relevance measure. In empirical study, we choose Symmetric Uncertainty as FF-Correlation measure, and choose Information Gain, Chi-Square, and Relief as three different FC-Relevance measures. Based on some real projects Eclipse and NASA, we implemented our framework and performed empirical studies to investigate the redundancy rate and the performance of the trained defect predictors. Final results verify the effectiveness of our proposed framework and further provide a guideline for achieving cost-effective feature selection when using our framework.
Sponsorship	IEEE Comput. Soc.
Starting Page	426
Ending Page	435
File Size	372480
Page Count	10
File Format	PDF
ISBN	9781479935758
ISSN	07303157
DOI	10.1109/COMPSAC.2014.66
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-21
Publisher Place	Sweden
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Software Gain measurement Redundancy Clustering algorithms Measurement uncertainty Complexity theory Feature Ranking Software Defect Prediction Feature Selection Feature Clustering
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Software

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