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Ensemble MultiBoost Based on RIPPER Classifier for Prediction of Imbalanced Software Defect Data

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 E nsemble M ultiBoost based on R IPPER classifier for prediction of imbalanced S oftware D efect 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.

Journal Article Type Article
Publication Date Aug 9, 2019
Journal IEEE Access
Print ISSN 2169-3536
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 7
Pages 110333-110343
Institution 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 : practical innovations, open solutions, 7, 110333-110343. https://doi.org/10.1109/access.2019.2934128
DOI https://doi.org/10.1109/access.2019.2934128
Keywords General Engineering; General Materials Science; General Computer Science
Publisher URL https://ieeexplore.ieee.org/document/8793088

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Copyright Statement
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/







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