Adaptive random testing by balancing

  • Authors:

  • T. Y. Chen;De Hao Huang;F.-C Kuo

  • Affiliations:

  • Swinburne University of Technology, Hawthorn, VIC, Australia;Swinburne University of Technology, Hawthorn, VIC, Australia;Swinburne University of Technology, Hawthorn, VIC, Australia

  • Venue:
  • Proceedings of the 2nd international workshop on Random testing: co-located with the 22nd IEEE/ACM International Conference on Automated Software Engineering (ASE 2007)
  • Year:
  • 2007

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Abstract

Adaptive Random Testing (ART) is an effective improvement of Random Testing (RT). It is based on the observation that failure-causing inputs tend to be clustered together. ART, therefore, proposes to have randomly selected test cases being more evenly spread throughout the input domain by employing the location information of the successful test cases (those that have been executed but do not reveal failures). Based on this intuition, several ART methods have been developed. However, the fault-detection capability of some ART methods is compromised in high dimensional input domains. To improve the fault-detection capability in high dimensional input domains, this paper proposes an innovative ART method using the notion of balancing. Simulation results show that the new method has improved the fault-detection capability in high dimensional input domains.