A Tabu Search hyper-heuristic strategy for t-way test suite generation

Zamil, Kamal Z. and Alkazemi, Basem Y. and Kendall, G. (2016) A Tabu Search hyper-heuristic strategy for t-way test suite generation. Applied Soft Computing, 44 . pp. 57-74. ISSN 1872-9681

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Abstract

This paper proposes a novel hybrid t-way test generation strategy (where t indicates interaction strength), called High Level Hyper-Heuristic (HHH). HHH adopts Tabu Search as its high level meta-heuristic and leverages on the strength of four low level meta-heuristics, comprising of Teaching Learning based Optimization, Global Neighborhood Algorithm, Particle Swarm Optimization, and Cuckoo Search Algorithm. HHH is able to capitalize on the strengths and limit the deficiencies of each individual algorithm in a collective and synergistic manner. Unlike existing hyper-heuristics, HHH relies on three defined operators, based on improvement, intensification and diversification, to adaptively select the most suitable meta-heuristic at any particular time. Our results are promising as HHH manages to outperform existing t-way strategies on many of the benchmarks.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/976345
Keywords: Software testing; t-way Testing; Hyper-heuristic; Particle Swarm Optimization; Cuckoo Search Algorithm; Teaching Learning based Optimization; Global Neighborhood Algorithm
Schools/Departments: University of Nottingham, Malaysia > Faculty of Science > School of Computer Science
University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1016/j.asoc.2016.03.021
Depositing User: Kendall, Graham
Date Deposited: 05 Feb 2018 10:55
Last Modified: 04 May 2020 20:02
URI: http://eprints.nottingham.ac.uk/id/eprint/49537

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