By the same authors

Dependent input sampling strategies: using metaheuristics for generating parameterised random sampling regimes

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Title of host publicationGECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference
DateAccepted/In press - 25 Mar 2018
DatePublished (current) - 2 Jul 2018
Pages1451-1458
Number of pages8
PublisherAssociation for Computing Machinery, Inc
Original languageEnglish
ISBN (Electronic)9781450356183

Abstract

Understanding extreme execution times is of great importance in gaining assurance in real-time embedded systems. The standard benchmark for dynamic testing'uniform randomised testing'is inadequate for reaching extreme execution times in these systems. Metaheuristics have been shown to be an effective means of directly searching for inputs with such behaviours but the increasing complexity of modern systems is now posing challenges to the effectiveness of this approach. The research reported in this paper investigates the use of metaheuristic search to discover biased random sampling regimes. Rather than search for test inputs, we search for distributions of test inputs that are then sampled. The search proceeds to discover and exploit relationships between test input variables, leading to sampling regimes where the distribution of a sampled parameter depends on the values of previously sampled input parameters. Our results show that test vectors indirectly generated from our dependent approach produce significantly more extreme (longer) execution times than those generated by direct metaheuristic searches.

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©2018 Association for Computing Machinery. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details

    Research areas

  • Genetic algorithms, Hill climbing, Metaheuristics, Simulated annealing, Temporal testing

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