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. (Published)
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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 |
|---|---|
| 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 |
| URI: | http://umpir.ump.edu.my/id/eprint/16832 |
| Download Statistic: | View Download Statistics |
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