Ensemble multiboost based on ripper classifier for prediction of imbalanced software defect data

He, Haitao; Zhang, Xu; Wang, Qian; Ren, Jiadong; Liu, Jiaxin; Zhao, Xiaolin; Cheng, Yongqiang

Abstract

Identifying defective software entities is essential to ensure software quality during software development. However, the high dimensionality and class distribution imbalance of software defect data seriously affect software defect prediction performance. In order to solve this problem, this paper proposes an Ensemble MultiBoost based on RIPPER classifier for prediction of imbalanced Software Defect data, called EMR_SD. Firstly, the algorithm uses principal component analysis (PCA) method to find out the most effective features from the original features of the data set, so as to achieve the purpose of dimensionality reduction and redundancy removal. Furthermore, the combined sampling method of adaptive synthetic sampling (ADASYN) and random sampling without replacement is performed to solve the problem of data class imbalance. This classifier establishes association rules based on attributes and classes, using MultiBoost to reduce deviation and variance, so as to achieve the purpose of reducing classification error. The proposed prediction model is evaluated experimentally on the NASA MDP public datasets and compared with existing similar algorithms. The results show that EMR-SD algorithm is superior to DNC, CEL and other defect prediction techniques in most evaluation indicators, which proves the effectiveness of the algorithm.

Citation

He, H., Zhang, X., Wang, Q., Ren, J., Liu, J., Zhao, X., & Cheng, Y. (2019). Ensemble multiboost based on ripper classifier for prediction of imbalanced software defect data. IEEE Access, 7, 110333-110343. https://doi.org/10.1109/ACCESS.2019.2934128

• Journal Article Type: Article
• Acceptance Date: Jul 30, 2019
• Online Publication Date: Aug 9, 2019
• Publication Date: Aug 9, 2019
• Deposit Date: Aug 30, 2019
• Publicly Available Date: Aug 30, 2019
• Journal: IEEE Access
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Pages: 110333-110343
• DOI: https://doi.org/10.1109/ACCESS.2019.2934128
• Keywords: General Engineering; General Materials Science; General Computer Science
• Public URL: https://hull-repository.worktribe.com/output/2505243
• Publisher URL: https://ieeexplore.ieee.org/document/8793088
• Contract Date: Aug 30, 2019