- Title
- Heterogeneous defect prediction through multiple kernel learning and ensemble learning
- Creator
- Li, Zhiqiang; Jing, Xiao-Yuan; Zhu, Xiaoke; Zhang, Hongyu
- Relation
- 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME 2017). Proceedings of the 33rd IEEE International Conference on Software Maintenance and Evolution (Shanghai, China 17-22 September, 2017) p. 91-102
- Publisher Link
- http://dx.doi.org/10.1109/ICSME.2017.19
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2017
- Description
- Heterogeneous defect prediction (HDP) aims to predict defect-prone software modules in one project using heterogeneous data collected from other projects. Recently, several HDP methods have been proposed. However, these methods do not sufficiently incorporate the two characteristics of the defect prediction data: (1) data could be linearly inseparable, and (2) data could be highly imbalanced. These two data characteristics make it challenging to build an effective HDP model. In this paper, we propose a novel Ensemble Multiple Kernel Correlation Alignment (EMKCA) based approach to HDP, which takes into consideration the two characteristics of the defect prediction data. Specifically, we first map the source and target project data into high dimensional kernel space through multiple kernel leaning, where the defective and non-defective modules can be better separated. Then, we design a kernel correlation alignment method to make the data distribution of the source and target projects similar in the kernel space. Finally, we integrate multiple kernel classifiers with ensemble learning to relieve the influence caused by class imbalance problem, which can improve the accuracy of the defect prediction model. Consequently, EMKCA owns the advantages of both multiple kernel learning and ensemble learning. Extensive experiments on 30 public projects show that EMKCA outperforms the related competing methods.
- Subject
- heterogeneous defect prediction; kernel correlation alignment; multiple kernel learning; ensemble learning; linearly inseparable; class imbalance
- Identifier
- http://hdl.handle.net/1959.13/1394559
- Identifier
- uon:33722
- Identifier
- ISBN:9781538609927
- Language
- eng
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