Statistical fault localization based on importance sampling

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages58-63
Number of pages6
ISBN (Electronic)9781509002870
DOIs
StatePublished - Mar 2 2016
EventIEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015 - Miami, United States
Duration: Dec 9 2015Dec 11 2015

Publication series

NameProceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015

Conference

ConferenceIEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
CountryUnited States
CityMiami
Period12/9/1512/11/15

Keywords

  • Fault localization
  • Importance sampling

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