An Empirical Study on the Effectiveness of Feature Selection for Cross-Project Defect Prediction

IEEE Access ◽

10.1109/access.2019.2895614 ◽

2019 ◽

Vol 7 ◽

pp. 35710-35718 ◽

Author(s):

Qiao Yu ◽

Junyan Qian ◽

Shujuan Jiang ◽

Zhenhua Wu ◽

Gongjie Zhang

Keyword(s):

Feature Selection ◽

Empirical Study ◽

Defect Prediction ◽

Selection For ◽

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An empirical study on pareto based multi-objective feature selection for software defect prediction

Journal of Systems and Software ◽

10.1016/j.jss.2019.03.012 ◽

2019 ◽

Vol 152 ◽

pp. 215-238 ◽

Author(s):

Chao Ni ◽

Xiang Chen ◽

Fangfang Wu ◽

Yuxiang Shen ◽

Qing Gu

Keyword(s):

Feature Selection ◽

Empirical Study ◽

Defect Prediction ◽

Software Defect Prediction ◽

Multi Objective ◽

Software Defect ◽

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A benchmark study on the effectiveness of search-based data selection and feature selection for cross project defect prediction

Information and Software Technology ◽

10.1016/j.infsof.2017.06.004 ◽

2018 ◽

Vol 95 ◽

pp. 296-312 ◽

Author(s):

Seyedrebvar Hosseini ◽

Burak Turhan ◽

Mika Mäntylä

Keyword(s):

Feature Selection ◽

Data Selection ◽

Defect Prediction ◽

Benchmark Study ◽

Selection For ◽

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Inter-release defect prediction with feature selection using temporal chunk-based learning: An empirical study

Applied Soft Computing ◽

10.1016/j.asoc.2021.107870 ◽

2021 ◽

Vol 113 ◽

pp. 107870

Author(s):

Md Alamgir Kabir ◽

Jacky Keung ◽

Burak Turhan ◽

Kwabena Ebo Bennin

Keyword(s):

Feature Selection ◽

Empirical Study ◽

Defect Prediction

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An empirical study of just-in-time defect prediction using cross-project models

Proceedings of the 11th Working Conference on Mining Software Repositories - MSR 2014 ◽

10.1145/2597073.2597075 ◽

2014 ◽

Author(s):

Takafumi Fukushima ◽

Yasutaka Kamei ◽

Shane McIntosh ◽

Kazuhiro Yamashita ◽

Naoyasu Ubayashi

Keyword(s):

Empirical Study ◽

Defect Prediction ◽

Just In Time ◽

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An Empirical Study on Wrapper-Based Feature Selection for Software Engineering Data

2013 12th International Conference on Machine Learning and Applications ◽

10.1109/icmla.2013.110 ◽

2013 ◽

Author(s):

Huanjing Wang ◽

Taghi M. Khoshgoftaar ◽

Amri Napolitano

Keyword(s):

Feature Selection ◽

Software Engineering ◽

Empirical Study ◽

Engineering Data ◽

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FeSCH: A Feature Selection Method using Clusters of Hybrid-data for Cross-Project Defect Prediction

2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC) ◽

10.1109/compsac.2017.127 ◽

2017 ◽

Author(s):

Chao Ni ◽

Wangshu Liu ◽

Qing Gu ◽

Xiang Chen ◽

Daoxu Chen

Keyword(s):

Feature Selection ◽

Feature Selection Method ◽

Selection Method ◽

Defect Prediction ◽

Hybrid Data ◽

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An Empirical Study of Category Skew on Feature Selection for Text Categorization

Advances in Artificial Intelligence - Lecture Notes in Computer Science ◽

10.1007/978-3-642-01818-3_35 ◽

2009 ◽

pp. 249-252

Author(s):

Mondelle Simeon ◽

Robert Hilderman

Keyword(s):

Feature Selection ◽

Empirical Study ◽

Text Categorization ◽

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An Empirical Study on the Stability of Feature Selection for Imbalanced Software Engineering Data

2012 11th International Conference on Machine Learning and Applications ◽

10.1109/icmla.2012.60 ◽

2012 ◽

Author(s):

Huanjing Wang ◽

Taghi M. Khoshgoftaar ◽

Amri Napolitano

Keyword(s):

Feature Selection ◽

Software Engineering ◽

Empirical Study ◽

Engineering Data ◽

Selection For ◽

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An Effective Comparative Framework for Cross-Project Defect Prediction Based on the Feature Selection Technique

Journal of KIISE ◽

10.5626/jok.2018.45.7.635 ◽

2018 ◽

Vol 45 (7) ◽

pp. 635-658

Author(s):

Duksan Ryu ◽

Jongmoon Baik

Keyword(s):

Feature Selection ◽

Defect Prediction ◽

Feature Selection Technique ◽

Selection Technique ◽

Comparative Framework ◽

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An Empirical Study on the Equivalence and Stability of Feature Selection for Noisy Software Defect Data

Proceedings of the 29th International Conference on Software Engineering and Knowledge Engineering ◽

10.18293/seke2017-097 ◽

2017 ◽

Author(s):

Zhou Xu ◽

Jin Liu ◽

Zhen Xia ◽

Peipei Yuan

Keyword(s):

Feature Selection ◽

Empirical Study ◽

Software Defect ◽

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