【摘要】 针对基于统计理论的故障定位模型SOBER,研究软件故障的自动定位技术.通过程序研究及大量实例分析,探明SOBER模型的局限性——因为谓词关联性问题而导致故障定位准确度不高,并提出一种新的关联谓词赋值偏好方法,并进行了实证研究.实验结果表明,该方法较好地解决了谓词干扰问题,从而提高了基于SOBER模型的故障定位准确率.更多还原

【Abstract】 Study automated localization of software bugs on the basis of an important statistical model-based bug localization,called SOBER.By program research and large numbers of instance analysis,find the SOBER’s limitation which will cause localization errors of software bugs because of predicate relativity.Come up with a new solution about evaluation bias of relevant predicates and conduct instance study.The results show that the study preferably solves the problem of predicate interference and greatly improves SOBER-based automated localization of program bugs.更多还原