Value-cognitive boosting with a support vector machine for cross-project defect prediction
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- 作者：Duksan Ryu ; Okjoo Choi ; Jongmoon Baik
- 关键词：Boosting ; Class imbalance ; Cross ; project defect prediction ; Transfer learning
- 刊名：Empirical Software Engineering
- 出版年：2016
- 出版时间：February 2016
- 年：2016
- 卷：21
- 期：1
- 页码：43-71
- 全文大小：1,072 KB
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Okjoo Choi (1)
Jongmoon Baik (1)
1. Department of Computer Science, Korea Advanced Institute of Science and Technology, 291 Daehak-ro (373-1 Guseong-dong), Yuseong-gu, Daejeon, 305-701, Republic of Korea - 刊物类别：Computer Science
- 刊物主题：Software Engineering, Programming and Operating Systems
Programming Languages, Compilers and Interpreters - 出版者：Springer Netherlands
- ISSN：1573-7616
文摘
It is well-known that software defect prediction is one of the most important tasks for software quality improvement. The use of defect predictors allows test engineers to focus on defective modules. Thereby testing resources can be allocated effectively and the quality assurance costs can be reduced. For within-project defect prediction (WPDP), there should be sufficient data within a company to train any prediction model. Without such local data, cross-project defect prediction (CPDP) is feasible since it uses data collected from similar projects in other companies. Software defect datasets have the class imbalance problem increasing the difficulty for the learner to predict defects. In addition, the impact of imbalanced data on the real performance of models can be hidden by the performance measures chosen. We investigate if the class imbalance learning can be beneficial for CPDP. In our approach, the asymmetric misclassification cost and the similarity weights obtained from distributional characteristics are closely associated to guide the appropriate resampling mechanism. We performed the effect size A-statistics test to evaluate the magnitude of the improvement. For the statistical significant test, we used Wilcoxon rank-sum test. The experimental results show that our approach can provide higher prediction performance than both the existing CPDP technique and the existing class imbalance technique. Keywords Boosting Class imbalance Cross-project defect prediction Transfer learning