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Multiple-components weights modelfor cross-project software defect prediction

机译：用于跨项目软件缺陷预测的多分量权重模型<？show [AQ =“” ID =“ Q1]”？>

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摘要

Software defect prediction (SDP) technology is receiving widely attention and most of SDP models are trained on data from the same project. However, at an early phase of the software lifecycle, there are little to no within-project training data to learn an available supervised defect-prediction model. Thus, cross-project defect prediction (CPDP), which is learning a defect predictor for a target project by using labelled data from a source project, has shown promising value in SDP. To better perform the CPDP, most current studies focus on filtering instances or selecting features to weaken the impact of irrelevant cross-project data. Instead, the authors propose a novel multiple-components weights (MCWs) learning model to analyse the varying auxiliary power of multiple components in a source project to construct a more precise ensemble classifiers for a target project. By combining the MCW model with kernel mean matching algorithm, their proposed approach adjusts the source-instance weights and source-component weights to jointly alleviate the negative impacts of irrelevant cross-project data. They conducted comprehensive experiments by employing 15 real-world datasets to demonstrate the advantages and effectiveness of their proposed approach.
机译：软件缺陷预测（SDP）技术正受到广泛关注，并且大多数SDP模型都针对来自同一项目的数据进行了培训。但是，在软件生命周期的早期阶段，几乎没有甚至没有项目内的培训数据来学习可用的监督式缺陷预测模型。因此，跨项目缺陷预测（CPDP）通过使用来自源项目的标记数据来学习目标项目的缺陷预测器，在SDP中显示出了可喜的价值。为了更好地执行CPDP，当前大多数研究都集中在过滤实例或选择功能以减弱无关的跨项目数据的影响。相反，作者提出了一种新颖的多分量权重（MCW）学习模型，以分析源项目中多个组件的变化辅助能力，从而为目标项目构建更精确的集成分类器。通过将MCW模型与核均值匹配算法相结合，他们提出的方法调整了源实例权重和源组件权重，以共同减轻无关的跨项目数据的负面影响。他们通过使用15个现实世界的数据集进行了全面的实验，以证明他们提出的方法的优势和有效性。

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  • 来源
    《Software, IET》 |2018年第4期|345-355|共11页
  • 作者

    Shaojian Qiu; Lu Lu; Siyu Jiang;

  • 作者单位

    School of Computer Science and Engineering, South China University of Technology, People's Republic of China;

    School of Computer Science and Engineering, South China University of Technology, People's Republic of China;

    School of Software Engineering, South China University of Technology, People's Republic of China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    learning (artificial intelligence); software quality;

    机译：学习（人工智能）;软件质量;

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