Kamal Z., Zamli and Fakhrud, Din and Kendall, Graham and Ahmed, Bestoun S. (2017) An Experimental Study of Hyper-heuristic Selection and Acceptance Mechanism for Combinatorial T-Way Test Suite Generation. Information Sciences, 399. pp. 121-153. ISSN 0020-0255. (Published)
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Abstract
Recently, many meta-heuristic algorithms have been proposed to serve as the basis of a t-way test generation strategy (where t indicates the interaction strength) including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Simulated Annealing (SA), Cuckoo Search (CS), Particle Swarm Optimization (PSO), and Harmony Search (HS). Although useful, meta-heuristic algorithms that make up these strategies often require specific domain knowledge in order to allow effective tuning before good quality solutions can be obtained. Hyper-heuristics provide an alternative methodology to meta-heuristics which permit adaptive selection and/or generation of meta-heuristics automatically during the search process. This paper describes our experience with four hyper-heuristic selection and acceptance mechanisms namely Exponential Monte Carlo with counter (EMCQ), Choice Function (CF), Improvement Selection Rules (ISR), and newly developed Fuzzy Inference Selection (FIS), using the t-way test generation problem as a case study. Based on the experimental results, we offer insights on why each strategy differs in terms of its performance.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Software testing; t-way testing; Hyper-heuristics; Meta-heuristics; Fuzzy Inference Selection |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Faculty/Division: | Faculty of Computer System And Software Engineering |
| Depositing User: | Mrs. Neng Sury Sulaiman |
| Date Deposited: | 17 Apr 2017 04:35 |
| Last Modified: | 15 Jan 2018 07:00 |
| URI: | http://umpir.ump.edu.my/id/eprint/14604 |
| Download Statistic: | View Download Statistics |
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