MVSE: Effort-Aware Heterogeneous Defect Prediction via Multiple-View Spectral Embedding

Zhou Xu, Sizhe Ye, Tao Zhang, Z. Xia, Shuai Pang, Yong Wang, Yutian Tang
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引用次数: 4

Abstract

Cross-Project Defect Prediction (CPDP) predicts defects in a target project using the defect information of the external project. Existing CPDP methods assume that the data of two projects share identical features. When cross-project data contain heterogeneous features, traditional CPDP methods become ineffective. In this paper, we propose a novel approach called Multiple-View Spectral Embedding (MVSE) to address the heterogeneous CPDP issue. MVSE treats the cross-project data as two different views and exploits the spectral embedding method to map the heterogeneous feature sets into a consistent space where the two mapped feature sets have maximal similarity. To evaluate MVSE in the realistic setting, we employ an effort-aware performance indicator that considers the cost of inspection in the context of heterogeneous CPDP scenario. We have conducted extensive experiments to compare MVSE with two state-of-the-art heterogeneous CPDP methods and within-project setting. The experiments on 94 cross project pairs show that MVSE achieves promising results.
基于多视点光谱嵌入的努力感知异质缺陷预测
跨项目缺陷预测(CPDP)使用外部项目的缺陷信息来预测目标项目中的缺陷。现有的CPDP方法假设两个项目的数据具有相同的特征。当跨项目数据包含异构特征时,传统的CPDP方法变得无效。在本文中,我们提出了一种称为多视图频谱嵌入(MVSE)的新方法来解决异构CPDP问题。MVSE将跨项目数据视为两个不同的视图,并利用谱嵌入方法将异构特征集映射到两个映射特征集具有最大相似性的一致空间。为了在现实环境中评估MVSE,我们采用了一个努力感知性能指标,该指标考虑了异构CPDP场景下的检查成本。我们进行了大量的实验,将MVSE与两种最先进的异构CPDP方法进行比较,并在项目环境中进行了比较。在94个交叉项目对上的实验表明,该方法取得了良好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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