Adaptive Random Testing Through Dynamic Partitioning

  • Authors:
  • T. Y. Chen;R. Merkel;G. Eddy;P. K. Wong

  • Affiliations:
  • Swinburne University of Technology, Australia;Swinburne University of Technology, Australia;The University of Melbourne, Australia;Hong Kong Institute of Vocational Education (Sha Tin), Hong Kong

  • Venue:
  • QSIC '04 Proceedings of the Quality Software, Fourth International Conference
  • Year:
  • 2004

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Abstract

Adaptive Random Testing (ART) describes a family of algorithms for generating random test cases that have been experimentally demonstrated to have greater fault-detection capacity than simple random testing. We outline and demonstrate two new ART algorithms, and demonstrate experimentally that they offer similar performance advantages, with considerably lower overhead than other ART algorithms.