@inproceedings{fbe4e5ffa9b3496782b27f42c98f19f2,
title = "Statistical fault localization based on importance sampling",
abstract = "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.",
keywords = "Fault localization, Importance sampling",
author = "Namin, {Akbar Siami}",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015 ; Conference date: 09-12-2015 Through 11-12-2015",
year = "2016",
month = mar,
day = "2",
doi = "10.1109/ICMLA.2015.91",
language = "English",
series = "Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "58--63",
booktitle = "Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015",
}