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Adaptive Random Testing

  • T. Y. Chen
  • H. Leung
  • I. K. Mak
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3321)

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • T. Y. Chen
    • 1
  • H. Leung
    • 2
  • I. K. Mak
    • 3
  1. 1.School of Information TechnologySwinburne University of TechnologyHawthornAustralia
  2. 2.Department of Computer ScienceNew Mexico State UniversityLas CrucesU.S.A.
  3. 3.School of Professional and Continuing EducationThe University of Hong KongHong Kong

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