Adaptive random testing by bisection and localization

  • Authors:

  • Johannes Mayer

  • Affiliations:

  • Dept. of Applied Information Processing, University of Ulm, Ulm, Germany

  • Venue:
  • FATES'05 Proceedings of the 5th international conference on Formal Approaches to Software Testing
  • Year:
  • 2005

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Abstract

Adaptive Random Testing (ART) denotes a family of test case generation algorithms that are designed to detect common failure patterns better than pure Random Testing. The best known ART algorithms, however, use many distance computations. Therefore, these algorithms are quite inefficient regarding runtime. New algorithms combining Adaptive Random Testing by Bisection and the principle of localization are presented. These algorithms heavily reduce the amount of distance computation while exhibiting very good performance measured in terms of the number of test cases necessary to detect the first failure.