Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/16318
Title: A Comprehensive Investigation of the Role of Imbalanced Learning for Software Defect Prediction
Authors: Song, Q
Guo, Y
Shepperd, M
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: IEEE Transactions on Software Engineering, 2018
Abstract: IEEE Context: Software defect prediction (SDP) is an important challenge in the field of software engineering, hence much research work has been conducted, most notably through the use of machine learning algorithms. However, class-imbalance typified by few defective components and many non-defective ones is a common occurrence causing difficulties for these methods. Imbalanced learning aims to deal with this problem and has recently been deployed by some researchers, unfortunately with inconsistent results.
URI: https://bura.brunel.ac.uk/handle/2438/16318
DOI: https://doi.org/10.1109/TSE.2018.2836442
ISSN: 0098-5589
Appears in Collections:Dept of Computer Science Embargoed Research Papers

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