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Adaptive Random Testing by Bisection and Localization

  • Johannes Mayer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3997)

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.

Keywords

Random Test Distance Computation Point Pattern Failure Pattern Coverage Ratio 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Johannes Mayer
    • 1
  1. 1.Dept. of Applied Information ProcessingUniversity of UlmUlmGermany

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