Srivisut, Komsan, Clark, John A. orcid.org/0000-0002-9230-9739 and Paige, Richard F. orcid.org/0000-0002-1978-9852 (2018) Dependent input sampling strategies:using metaheuristics for generating parameterised random sampling regimes. In: GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference. 2018 Genetic and Evolutionary Computation Conference, GECCO 2018, 15-19 Jul 2018 ACM , JPN , pp. 1451-1458.
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | ©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 |
Keywords: | Genetic algorithms,Hill climbing,Metaheuristics,Simulated annealing,Temporal testing |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 29 Aug 2018 15:50 |
Last Modified: | 16 Oct 2024 10:59 |
Published Version: | https://doi.org/10.1145/3205455.3205495 |
Status: | Published |
Publisher: | ACM |
Identification Number: | 10.1145/3205455.3205495 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:135047 |