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Thanh Tung Khuat
University of Science and Technology
Computer Science & IT Electrical & Electronics Engineering

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Ensemble learning for software fault prediction problem with imbalanced data Thanh Tung Khuat; My Hanh Le
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 4: August 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (369.606 KB) | DOI: 10.11591/ijece.v9i4.pp3241-3246

Abstract

Fault prediction problem has a crucial role in the software development process because it contributes to reducing defects and assisting the testing process towards fault-free software components. Therefore, there are a lot of efforts aiming to address this type of issues, in which static code characteristics are usually adopted to construct fault classification models.  One of the challenging problems influencing the performance of predictive classifiers is the high imbalance among patterns belonging to different classes. This paper aims to integrate the sampling techniques and common classification techniques to form a useful ensemble model for the software defect prediction problem. The empirical results conducted on the benchmark datasets of software projects have shown the promising performance of our proposal in comparison with individual classifiers.
Co-Authors My Hanh Le