DocumentCode :
2302630
Title :
Identifying Infeasible GUI Test Cases Using Support Vector Machines and Induced Grammars
Author :
Gove, Robert ; Faytong, Jorge
Author_Institution :
Human-Comput. Interaction Lab., Univ. of Maryland, College Park, MD, USA
fYear :
2011
fDate :
21-25 March 2011
Firstpage :
202
Lastpage :
211
Abstract :
Model-based GUI software testing is an emerging paradigm for automatically generating test suites. In the context of GUIs, a test case is a sequence of events to be executed which may detect faults in the application. However, a test case may be infeasible if one or more of the events in the event sequence are disabled or made inaccessible by a previously executed event (e.g., a button may be disabled until another GUI widget enables it). These infeasible test cases terminate prematurely and waste resources, so software testers would like to modify the test suite execution to run only feasible test cases. Current techniques focus on repairing the test cases to make them feasible, but this relies on executing all test cases, attempting to repair the test cases, and then repeating this process until a stopping condition has been met. We propose avoiding infeasible test cases altogether by predicting which test cases are infeasible using two supervised machine learning methods: support vector machines (SVMs) and grammar induction. We experiment with three feature extraction techniques and demonstrate the success of the machine learning algorithms for classifying infeasible GUI test cases in several subject applications. We further demonstrate a level of robustness in the algorithms when training and classifying test cases of different lengths.
Keywords :
grammars; graphical user interfaces; learning (artificial intelligence); program testing; software fault tolerance; support vector machines; GUI test case; event sequence; fault detection; feature extraction; grammar induction; model-based GUI software testing; supervised machine learning; support vector machine; test case repair; test suite execution; Grammar; Graphical user interfaces; Machine learning; Machine learning algorithms; Software; Support vector machines; Testing; GUI testing; event based testing; grammar induction; machine learning; software testing; support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Software Testing, Verification and Validation Workshops (ICSTW), 2011 IEEE Fourth International Conference on
Conference_Location :
Berlin
Print_ISBN :
978-1-4577-0019-4
Electronic_ISBN :
978-0-7695-4345-1
Type :
conf
DOI :
10.1109/ICSTW.2011.73
Filename :
5954411
Link To Document :
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