摘要
目前的错误定位方法大多数解决的是单错误定位。然而,错误之间是相互关联的,如何找到这些语句与测试结果之间的关联关系和错误之间的关联关系,并减轻偶然性正确的测试用例和相似测试用例对语句可疑度的影响,对提高多错误定位的效率至关重要。为了解决以上问题,提出了基于深度卷积网络的多错误定位方法,通过一种特殊结构的深度卷积网络得到一组准确度比较高的语句可疑度,再将其应用于前向切片和后向切片中,寻找到错误与错误之间的关联定位多错误。实验表明,所提方法的多错误定位效率高于目前存在的经典的错误定位方法的错误定位效率。
Most of the current fault localization methods solve single fault localization,but the faults are related to each other.How to find the relationship between these faults and the test results and the relationship between the faults,and reduce the impact of coincidental correct test cases and similar test cases on the suspiciousness of sentences is very important to improve the efficiency of multiple fault localization.In order to solve the above problems,this paper proposes a multiple fault localization method based on deep convolutional network.A set of suspiciousness with high accuracy is obtained through a deep convolutional network with a special structure,and then applied to forward slicing and backward slicing,the correlation between faults and faults is found to locate multiple faults.Experiments show that the multiple fault localization efficiency of the method in this paper is stronger than that of the existing classic fault localization methods.
作者
张慧
ZHANG Hui(School of Computer Science,Jiangsu University of Science and Technology,Zhenjiang,Jiangsu 212003,China)
出处
《计算机科学》
CSCD
北大核心
2021年第S02期88-92,共5页
Computer Science
关键词
错误定位
深度卷积网络
切片
Fault localization
Deep convolutional network
Slicing
