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

IEEE Access

卷:7

Xiaolin Zhao¹  Haitao He²  Jiadong Ren²  Jiaxin Liu²  Xu Zhang²  Qian Wang²  Yongqiang Cheng³ 

[1] Beijing Key Laboratory of Software Security Engineering Technology, School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China;

[2] Computer Virtual Technology and System Integration Laboratory of Hebei Province, College of Information Science and Engineering, Yanshan University, Qinhuangdao, China;

[3] Department of Computer Science, University of Hull, Hull, U.K.;

关键词: Software defect prediction;    class imbalance;    combined sampling;    rule learning;    MultiBoost;   

DOI  :  10.1109/ACCESS.2019.2934128

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown