基于缺陷检测难度的测试用例检错能力模型
发布时间：2018-09-04 16:09
【摘要】：在经典的变异评分计算过程中,因为不考虑被播种软件缺陷的检测难度而使得变异评分的可信性受到质疑.因此提出一种基于缺陷检测难度评价测试用例集合的方法.以logistic回归为基础,利用经验回归方程建立缺陷的识别概率与缺陷检测难度之间的数量关系.借助关系曲线下的面积,变异评分被重新定义.新定义的变异评分不但不受缺陷样本的检测难度影响,而且规避了因等价变异体的出现而使得经典变异评分的计算不准确的问题.
[Abstract]:In the classical variation score calculation process, the credibility of the variation score is questioned because it does not consider the difficulty of detecting the defects of the sown software. Therefore, a method of evaluating test case set based on defect detection difficulty is proposed. Based on logistic regression, the quantitative relationship between defect identification probability and defect detection difficulty is established by using empirical regression equation. The variation score is redefined with the aid of the area under the relational curve. The newly defined variation score is not only not affected by the difficulty of detecting defective samples, but also avoids the problem that the calculation of classical variation score is inaccurate because of the appearance of equivalent variants.
【作者单位】： 北京邮电大学网络与交换国家重点实验室;北京邮电大学自动化学院;
【基金】：国家自然科学基金项目(61202080) 广西云计算与大数据协同创新中心 广西高校云计算与复杂系统重点实验室资助(YD16508)
【分类号】：TP311.53
，
本文编号：2222674
[Abstract]:In the classical variation score calculation process, the credibility of the variation score is questioned because it does not consider the difficulty of detecting the defects of the sown software. Therefore, a method of evaluating test case set based on defect detection difficulty is proposed. Based on logistic regression, the quantitative relationship between defect identification probability and defect detection difficulty is established by using empirical regression equation. The variation score is redefined with the aid of the area under the relational curve. The newly defined variation score is not only not affected by the difficulty of detecting defective samples, but also avoids the problem that the calculation of classical variation score is inaccurate because of the appearance of equivalent variants.
【作者单位】： 北京邮电大学网络与交换国家重点实验室;北京邮电大学自动化学院;
【基金】：国家自然科学基金项目(61202080) 广西云计算与大数据协同创新中心 广西高校云计算与复杂系统重点实验室资助(YD16508)
【分类号】：TP311.53
，
本文编号：2222674
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