Statistical Fault Localization based on Importance Sampling

被引:3
作者
Namin, Akbar Siami [1 ]
机构
[1] Texas Tech Univ, Dept Comp Sci, Lubbock, TX 79409 USA
来源
2015 IEEE 14TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA) | 2015年
关键词
Fault Localization; Importance Sampling;
D O I
10.1109/ICMLA.2015.91
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
This paper presents a novel probabilistic approach for the fault localization challenge based on importance sampling. The iterative approach utilizes test results and execution profiles to estimate the likelihood of suspiciousness of program statements. Over a few iterations of probability updates and sampling, the procedure directs its attention towards those statements that are more likely to be faulty. The proposed approach is designed to be more sensitive to failing test cases in comparison to passing test cases. The effectiveness of the proposed stochastic approach is evaluated through two case studies and the results are compared against other popular fault localization methods.
引用
收藏
页码:58 / 63
页数:6
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