Adaptive Random Testing with CG Constraint

  • Authors:

  • F. T. Chan;K. P. Chan;T. Y. Chen;S. M. Yiu

  • Affiliations:

  • University of Hong Kong;University of Hong Kong;Swinburne University of Technology;University of Hong Kong

  • Venue:
  • COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Workshops and Fast Abstracts - Volume 02
  • Year:
  • 2004

  • Adaptive random testing by balancing

    Proceedings of the 2nd international workshop on Random testing: co-located with the 22nd IEEE/ACM International Conference on Automated Software Engineering (ASE 2007)

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Abstract

In this paper, we introduce a C. G. constraint on Adaptive Random Testing (ART) for programs with numerical input. One rationale behind Adaptive Random Testing is to have the test candidates to be as widespread over the input domain as possible. However, the computation may be quite expensive in some cases. The C. G. constraint is introduced to maintain the wide-spreadness while reducing the computation requirement in terms of number of distance measures. Three variations of C. G. constraints and their performance when compared with ART are discussed.