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A Tabu Search Hyper-Heuristic for t-way Test Suite Generation

Kamal Z., Zamli and Alkazemi, Basem Y. and Kendall, Graham (2016) A Tabu Search Hyper-Heuristic for t-way Test Suite Generation. Applied Soft Computing, 44. pp. 57-74. ISSN 1568-4946

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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
Uncontrolled Keywords: Software testing; t-way Testing; Hyper-heuristic; Particle Swarm Optimization; Cuckoo Search Algorithm; Teaching Learning based Optimization; Global Neighborhood Algorithm
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Faculty of Computer System And Software Engineering
Centre of Excellence: IBM Centre of Excellence
Depositing User: Prof. Dr. Kamal Zuhairi Zamli
Date Deposited: 28 Feb 2017 06:52
Last Modified: 14 Sep 2018 08:38
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