Feature selections for effectively localizing faulty events in GUI applications

Xiaozhen Xue, Yulei Pang, Akbar Siami Namin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

Due to the complex causality of failure and the special characteristics of test cases, the faults in GUI (Graphic User Interface) applications are difficult to localize. This paper adapts feature selection algorithms to localize GUI-related faults in a given program. Features are defined as the subsequences of events executed. By employing statistical feature ranking techniques, the events can be ranked by the suspiciousness of events being responsible to exhibit faulty behavior. The features defined in a given source code implementing (event handle) the underlying event are then ranked in suspiciousness order. The evaluation of the proposed technique based on some open source Java projects verified the effectiveness of this feature selection based fault localization technique for GUI applications.

Original languageEnglish
Title of host publicationProceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014
EditorsCesar Ferri, Guangzhi Qu, Xue-wen Chen, M. Arif Wani, Plamen Angelov, Jian-Huang Lai
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages306-311
Number of pages6
ISBN (Electronic)9781479974153
DOIs
StatePublished - Feb 5 2014
Event2014 13th International Conference on Machine Learning and Applications, ICMLA 2014 - Detroit, United States
Duration: Dec 3 2014Dec 6 2014

Publication series

NameProceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014

Conference

Conference2014 13th International Conference on Machine Learning and Applications, ICMLA 2014
CountryUnited States
CityDetroit
Period12/3/1412/6/14

Keywords

  • GUI
  • faults localization
  • feature selection

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  • Cite this

    Xue, X., Pang, Y., & Namin, A. S. (2014). Feature selections for effectively localizing faulty events in GUI applications. In C. Ferri, G. Qu, X. Chen, M. A. Wani, P. Angelov, & J-H. Lai (Eds.), Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014 (pp. 306-311). [7033132] (Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA.2014.55