Adaptive random testing

  • Authors:

  • T. Y. Chen;H. Leung;I. K. Mak

  • Affiliations:

  • School of Information Technology, Swinburne University of Technology, Hawthorn, Victoria, Australia;Department of Computer Science, New Mexico State University, Las Cruces, NM;School of Professional and Continuing Education, The University of Hong Kong, Hong Kong

  • Venue:
  • ASIAN'04 Proceedings of the 9th Asian Computing Science conference on Advances in Computer Science: dedicated to Jean-Louis Lassez on the Occasion of His 5th Cycle Birthday
  • Year:
  • 2004

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this paper, we introduce an enhanced form of random testing called Adaptive Random Testing. Adaptive random testing seeks to distribute test cases more evenly within the input space. It is based on the intuition that for non-point types of failure patterns, an even spread of test cases is more likely to detect failures using fewer test cases than ordinary random testing. Experiments are performed using published programs. Results show that adaptive random testing does outperform ordinary random testing significantly (by up to as much as 50%) for the set of programs under study. These results are very encouraging, providing evidences that our intuition is likely to be useful in improving the effectiveness of random testing.