结合数据选择的多源跨项目缺陷预测

doi:10.11896/jsjkx.210800160

摘要/Abstract

摘要： 多源跨项目缺陷预测(Multi-sources Cross Project Defect Prediction,MCPDP)旨在使用多个来自其他项目(源项目)的历史数据来预测目标项目中软件模块出现缺陷的可能性。该研究解决了缺陷预测建模的冷启动问题,为新建软件或缺乏历史数据的软件系统建立缺陷预测模型提供了解决方案。对于进一步提高跨项目缺陷预测的准确性,源数据选择被认为是一条有效途径。因此,文中对数据选择的多源跨项目缺陷预测方法进行了研究,该方法包括两个步骤:1)源数据特征对齐;2)改进最大均值测度,实现源数据筛选。为了验证提出的方法的有效性,在AEEEM,Relink,NASA,SOFTLAB这4个公开数据集进行实验,结果表明所提方法在F-measure指标上比基线方法分别提高了4%和5%,证明该方法具有较好的性能。

关键词: 多源域, 跨项目, 缺陷预测, 数据选择, 特征对齐

Abstract: Multi-sources cross project defect prediction(MCPDP) aims to use multiple historical data from other projects(source projects) to predict the likelihood of defects in software modules in the target project.The research solves the cold start problem of defect prediction modeling and provides a solution for establishing defect prediction model for new software or software system lacking historical data.Source data selection is considered to be an effective way to further improve the accuracy of cross-project defect prediction.Therefore,a multi-source cross-project defect prediction method for data selection is studied in this paper.The method includes two steps:1) feature alignment of source data;2) improve the maximum mean measure to realize source data screening.In order to verify the effectiveness of the proposed method,experiments are carried out on four public data sets,namely AEEEM,Relink,NASA and SOFTLAB.The results show that the proposed method improves the F-measure index by 4% and 5% respectively compared with the baseline method,which proves that the proposed method has good performance.

Key words: Multi-source domain, Across projects, Defect prediction, Data selection, Feature alignment

中图分类号:

TP311

邓建华, 王炜. 结合数据选择的多源跨项目缺陷预测[J]. 计算机科学, 2022, 49(11A): 210800160-7. https://doi.org/10.11896/jsjkx.210800160

DENG Jian-hua, WANG Wei. Multi-source Cross-project Defect Prediction with Data Selection[J]. Computer Science, 2022, 49(11A): 210800160-7. https://doi.org/10.11896/jsjkx.210800160

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