Enhancing adaptive random testing in high dimensional input domains

  • Authors:

  • F. C. Kuo;T. Y. Chen;H. Liu;W. K. Chan

  • Affiliations:

  • University of Wollongong, Wollongong, NSW, Australia;Swinburne University of Technology, Hawthorn, VIC, Australia;Swinburne University of Technology, Hawthorn, VIC, Australia;City University of Hong Kong, Hong Kong

  • Venue:
  • Proceedings of the 2007 ACM symposium on Applied computing
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

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.