Enhancing adaptive random testing in high dimensional input domains

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Enhancing adaptive random testing in high dimensional input domains

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Symposium on Applied Computing archive
Proceedings of the 2007 ACM symposium on Applied computing table of contents

Seoul, Korea

SESSION: Software engineering table of contents

Pages: 1467 - 1472

Year of Publication: 2007

ISBN:1-59593-480-4

Authors

F. C. Kuo	University of Wollongong, Wollongong, NSW, Australia
T. Y. Chen	Swinburne University of Technology, Hawthorn, VIC, Australia
H. Liu	Swinburne University of Technology, Hawthorn, VIC, Australia
W. K. Chan	City University of Hong Kong, Hong Kong

Sponsor

SIGAPP: ACM Special Interest Group on Applied Computing

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 7, Downloads (12 Months): 51, Citation Count: 5

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ABSTRACT

Adaptive random testing (ART) is an enhancement of random testing (RT). It can detect failures more effectively than RT when failure-causing inputs are clustered. Having test cases both randomly selected and evenly spread is the key to the success of ART. Recently, it has been found that the dimensionality of the input domain could have an impact on the effectiveness of ART. The effectiveness of some ART methods may deteriorate when the dimension is high. In this paper, we work on one particular ART method, namely Fixed-Sized-Candidate-Set ART (FSCS-ART) and show how it can be enhanced for high dimensional domains. Since the cause of the problems for FSCS-ART may also be valid for some other ART methods, our solutions to the high dimension problems of FSCS-ART may be applicable for improving other ART methods.

REFERENCES

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CITED BY 5

	Heng Lu, A Context-Oriented Framework for Software Testing in Pervasive Environment, Companion to the proceedings of the 29th International Conference on Software Engineering, p.77-78, May 20-26, 2007
	Tsong Yueh Chen , Fei-Ching Kuo , Huai Liu, Distributing test cases more evenly in adaptive random testing, Journal of Systems and Software, v.81 n.12, p.2146-2162, December, 2008
	Frédéric Dadeau , Yves Ledru , Lydie Du Bousquet, Directed random reduction of combinatorial test suites, Proceedings of the 2nd international workshop on Random testing: co-located with the 22nd IEEE/ACM International Conference on Automated Software Engineering (ASE 2007), November 06-06, 2007, Atlanta, Georgia
	T. Y. Chen , De Hao Huang , F.-C. Kuo , R. G. Merkel , Johannes Mayer, Enhanced lattice-based adaptive random testing, Proceedings of the 2009 ACM symposium on Applied Computing, March 08-12, 2009, Honolulu, Hawaii
	Fei-Ching Kuo , Tsong Yueh Chen , Huai Liu , Wing Kwong Chan, Enhancing adaptive random testing for programs with high dimensional input domains or failure-unrelated parameters, Software Quality Control, v.16 n.3, p.303-327, September 2008