On the relative value of data resampling approaches for software defect prediction-Reference-Cited by-同舟云学术

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On the relative value of data resampling approaches for software defect prediction

Published:2018-06-21 Issue:2 Volume:24 Page:602-636
ISSN:1382-3256
Container-title:Empirical Software Engineering
language:en
Short-container-title:Empir Software Eng

Author:

Bennin Kwabena Ebo^ORCID,Keung Jacky W.,Monden Akito

Funder

City University of Hong Kong

JSPS KAKENHI

General Research Fund of the Research Grants Council of Hong Kong

City University of Hong Kong (HK)

Publisher

Springer Science and Business Media LLC

Subject

Software

Link

http://link.springer.com/content/pdf/10.1007/s10664-018-9633-6.pdf

Reference71 articles.

1. Agrawal A, Menzies T (2017) Better data is better than better data miners (benefits of tuning smote for defect prediction). arXiv: 1705.03697

2. Arisholm E, Briand LC, Johannessen EB (2010) A systematic and comprehensive investigation of methods to build and evaluate fault prediction models. J Syst Softw 83 (1):2–17

3. Barua S, Md MI, Yao Xi, Murase K (2014) Mwmote–majority weighted minority oversampling technique for imbalanced data set learning. IEEE Trans Knowl Data Eng 26(2):405–425

4. Bennin K, Keung J, Monden A, Phannachitta P, Mensah S (2017) The significant effects of data sampling approaches on software defect prioritization and classification. In: 11th international symposium on empirical software engineering and measurement, ESEM 2017

5. Bennin KE, Keung J, Monden A, Kamei Y, Ubayashi N (2016) Investigating the effects of balanced training and testing datasets on effort-aware fault prediction models. In: 2016 IEEE 40th annual Computer software and applications conference (COMPSAC), vol 1. IEEE, pp 154–163

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