Ebrahim Bagheri – Publication Page

Machine Learning-based Software Testing: Towards a Classification Framework

Mahdi Noorian and Ebrahim Bagheri and Weichang Du
Reference:
Mahdi Noorian; Ebrahim Bagheri and Weichang Du Machine Learning-based Software Testing: Towards a Classification Framework. In SEKE, pages 225-229, 2011.
Links to Publication:
Abstract:
Software Testing (ST) processes attempt to verify and validate the capability of a software system to meet its required attributes and functionality. As software systems become more complex, the need for automated software testing methods emerges. Machine Learning (ML) techniques have shown to be quite useful for this automation process. Various works have been presented in the junction of ML and ST areas. The lack of general guidelines for applying appropriate learning methods for software testing purposes is our major motivation in this current paper. In this paper, we introduce a classification framework which can help to systematically review research work in the ML and ST domains. The proposed framework dimensions are defined using major characteristics of existing software testing and machine learning methods. Our framework can be used to effectively construct a concrete set of guidelines for choosing the most appropriate learning method and applying it to a distinct stage of the software testing life-cycle for automation purposes.
Bibtex Entry:
@inproceedings{DBLP:conf/seke/NoorianBD11, author = {Mahdi Noorian and Ebrahim Bagheri and Weichang Du}, title = {Machine Learning-based Software Testing: Towards a Classification Framework}, booktitle = {SEKE}, year = {2011}, pages = {225-229}, crossref = {DBLP:conf/seke/2011}, bibsource = {DBLP, http://dblp.uni-trier.de}, abstract = { Software Testing (ST) processes attempt to verify and validate the capability of a software system to meet its required attributes and functionality. As software systems become more complex, the need for automated software testing methods emerges. Machine Learning (ML) techniques have shown to be quite useful for this automation process. Various works have been presented in the junction of ML and ST areas. The lack of general guidelines for applying appropriate learning methods for software testing purposes is our major motivation in this current paper. In this paper, we introduce a classification framework which can help to systematically review research work in the ML and ST domains. The proposed framework dimensions are defined using major characteristics of existing software testing and machine learning methods. Our framework can be used to effectively construct a concrete set of guidelines for choosing the most appropriate learning method and applying it to a distinct stage of the software testing life-cycle for automation purposes. } }




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