An Improved SDA Based Defect Prediction Framework for Both Within-Project and Cross-Project Class-Imbalance Problems
An Improved SDA Based Defect Prediction Framework for Both Within-Project and Cross-Project Class-Imbalance Problems
复制标题
针对项目内和跨项目类不平衡问题的改进的基于 SDA 的缺陷预测框架
DOI：
10.1109/tse.2016.2597849
复制
发表时间：
2017-04-01
影响因子：
7.4
通讯作者：
Xu, Baowen
中科院分区：
文献类型：
--
作者：
Jing, Xiao-Yuan;Wu, Fei;Xu, Baowen
背景。解决项目内软件缺陷预测（SDP）的类不平衡问题是一个重要的研究主题。尽管已经提出了某些班级不平衡学习方法，但仍存在改进的空间。对于跨项目SDP，我们发现类不平衡的来源通常会导致缺陷实例分类。但是，只有一项工作关注这个跨项目的班级不平衡问题。客观的。我们旨在为项目内部和跨项目阶级不平衡问题提供有效的解决方案。方法。引入了一种有效的特征学习方法，以解决问题。它可以从原始指标中学习具有更强大的分类能力的功能。对于项目内预测，我们改善了SDA来实现平衡子类，并提出了改进的SDA（ISDA）方法。对于跨项目预测，我们采用半监督的传输组件分析（SSTCA）方法来使源数据的分布和目标数据的分布保持一致，并提出了SSTCAI-ISDA预测方法。结果。在四个广泛使用的数据集上进行的广泛实验表明：1）基于ISDA的解决方案的性能要比其他针对性类别不平衡问题的最先进方法更好； 2）提出针对跨项目类别不平衡问题的SSTCAI-ISDA明显优于相关的方法。结论。项目内和跨项目的类别不平衡问题极大地影响了预测性能，我们为这两个问题提供了统一有效的预测框架。
Background. Solving the class-imbalance problem of within-project software defect prediction (SDP) is an important research topic. Although some class-imbalance learning methods have been presented, there exists room for improvement. For cross-project SDP, we found that the class-imbalanced source usually leads to misclassification of defective instances. However, only one work has paid attention to this cross-project class-imbalance problem. Objective. We aim to provide effective solutions for both within-project and cross-project class-imbalance problems. Method. Subclass discriminant analysis (SDA), an effective feature learning method, is introduced to solve the problems. It can learn features with more powerful classification ability from original metrics. For within-project prediction, we improve SDA for achieving balanced subclasses and propose the improved SDA (ISDA) approach. For cross-project prediction, we employ the semi-supervised transfer component analysis (SSTCA) method to make the distributions of source and target data consistent, and propose the SSTCA+ISDA prediction approach. Results. Extensive experiments on four widely used datasets indicate that: 1) ISDA-based solution performs better than other state-of-the-art methods for within-project class-imbalance problem; 2) SSTCA+ISDA proposed for cross-project class-imbalance problem significantly outperforms related methods. Conclusion. Within-project and cross-project class-imbalance problems greatly affect prediction performance, and we provide a unified and effective prediction framework for both problems.