基于非参数假设检验的程序缺陷定位方法

调查了西门子测试集中的程序失败用例的规模和谓词评估偏差的分布.结果表明,中心极限定理的前提和参数假设检验的假设条件无法得到普遍满足.所以,已有的基于参数假设检验的方法存在潜在的问题.针对这一现象,提出了基于非参数假设检验定位程序缺陷的方法.实验结果表明,该方法在非正态分布的样本集上仍具有较好的适用性,且在缺陷定位效率上优于已有的基于程序谓词的缺陷定位技术.同时比较了基于2种常用的非参数假设检验模型的缺陷定位方法的效率.一种方法基于Kolmogorov-Smirnov检验,另一种方法基于Cramér-von-Mises准则.比较结果表明,在西门子测试集上,基于Kolmogorov-Smirnov检验的方法在缺陷定位效率上优于基于Cramér-von-Mises准则的方法.

Fault localization based on non-parametric hypothesis testing model

The size of failed runs and the distributions of the evaluation biases on the Siemens suite were investigated.The empirical results show that the precondition for the central limit theorem and the assumption on feature spectra forming normal distributions are not well-supported by empirical data.Thus,the previous method based on parametric hypothesis testing has a potential problem.New approaches based on non-parametric hypothesis testing models were proposed.The empirical results on the Siemens suite indicate that these approaches can outperform existing predicate-based statistical fault localization techniques,especially on non-normal distributions.The effectiveness comparison between two methods based on two popular non-parametric hypothesis testing models was also investigated.One method was based on the Kolmogorov-Smirnov test and the other was based on Cramér-von-Mises criterion.The comparison results show that the method based on the Kolmogorov-Smirnov test consistently outperforms that based on the Cramér-von-Mises criterion in the task of fault localization.