北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2023, Vol. 46 ›› Issue (4): 116-122.

• 研究报告 • 上一篇    下一篇

一种基于分类代价的软件缺陷评估方法

谢丽霞1,刘思雨1,杨宏宇1,2,张良3   

  1. 1. 中国民航大学 计算机科学与技术学院;
    2. 中国民航大学 安全科学与工程学院  3. 亚利桑那大学 信息学院
  • 收稿日期:2022-08-02 修回日期:2022-09-08 出版日期:2023-08-28 发布日期:2023-08-24
  • 通讯作者: 杨宏宇 E-mail:yhyxlx@hotmail.com

A Software Defect Assessment Method with Classification Cost

XIE Lixia1, LIU Siyu1, YANG Hongyu1,2, ZHANG Liang3   

  • Received:2022-08-02 Revised:2022-09-08 Online:2023-08-28 Published:2023-08-24
  • Contact: Hong-Yu YANG E-mail:yhyxlx@hotmail.com

摘要: 针对现有软件缺陷评估方法无法根据分类结果生成处理优先级且基于样本的类不平衡方法易造成重要数据丢失等问题提出一种基于分类代价的软件缺陷评估方法首先通过样本噪声处理样本分布归一化样本平衡重构和特征降维 4 个过程处理多源样本得到预处理样本然后计算预处理样本在多分类器上的评价指标和指标权重根据评价指标和指标权重计算分类代价并构建一个异质加权投票模型最后使用异质加权投票模型计算缺陷概率值并评估样本的缺陷程度实验结果表明所提方法得到的缺陷概率值和缺陷等级与真实样本符合度较高能够准确反映代码模块的缺陷等级

关键词: 缺陷评估 , 类不平衡 , 分类代价 , 异质加权投票 , 缺陷等级

Abstract: To solve the problems that the existing software defect assessment methods do not generate processing priority according to the dichotomous classification results, and the sample-based class imbalanced processing methods are prone to cause important data loss, a software defect assessment method with classification cost is proposed. First, preprocessed samples are obtained by processing multi-source samples through four phases: sample noise processing, sample distribution normalization, sample balance reconstruction and feature dimensionality reduction. Then, the evaluation indexes and index weights on multiple classifiers are calculated to obtain the classification cost, and the heterogeneous weighted voting model is constructed through the classification cost. Finally, the heterogeneous weighted voting model is used to calculate the defective probability values and assess the levels of the defective samples. The experimental results show that the defective probability values and defect levels obtained by the proposed method are more consistent with the actual situation of the real samples, which can accurately reflect the defect levels of the code modules.

Key words: defect assessment, class imbalance , classification cost , heterogeneous weighted voting , defect level

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