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Using Coding-Based Ensemble Learning to Improve Software Defect Prediction

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3 Author(s)
Zhongbin Sun ; Dept. of Comput. Sci. & Technol., Xi''an Jiaotong Univ., Xi''an, China ; Qinbao Song ; Xiaoyan Zhu

Using classification methods to predict software defect proneness with static code attributes has attracted a great deal of attention. The class-imbalance characteristic of software defect data makes the prediction much difficult; thus, a number of methods have been employed to address this problem. However, these conventional methods, such as sampling, cost-sensitive learning, Bagging, and Boosting, could suffer from the loss of important information, unexpected mistakes, and overfitting because they alter the original data distribution. This paper presents a novel method that first converts the imbalanced binary-class data into balanced multiclass data and then builds a defect predictor on the multiclass data with a specific coding scheme. A thorough experiment with four different types of classification algorithms, three data coding schemes, and six conventional imbalance data-handling methods was conducted over the 14 NASA datasets. The experimental results show that the proposed method with a one-against-one coding scheme is averagely superior to the conventional methods.

Note: In the print edition, this paper appears with the incorrect publication title in the running head. The correct publication title is IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS-PART C: APPLICATIONS AND REVIEWS.  

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Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on  (Volume:42 ,  Issue: 6 )