An experimental study of hyper-heuristic selection and acceptance mechanism for combinatorial t-way test suite generation
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 Optimizat...
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| Format: | Article |
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Elsevier
2017
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| Online Access: | https://eprints.nottingham.ac.uk/49538/ |
| _version_ | 1848798019473899520 |
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| author | Zamil, Kamal Z. Din, Fakhrud Kendall, Graham Ahmed, Bestoun S. |
| author_facet | Zamil, Kamal Z. Din, Fakhrud Kendall, Graham Ahmed, Bestoun S. |
| author_sort | Zamil, Kamal Z. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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, metaheuristic algorithms that make up these strategies often require specific domain knowledge in order to allow effective tuning before good quality solutions can be obtained. Hyperheuristics 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. |
| first_indexed | 2025-11-14T20:13:07Z |
| format | Article |
| id | nottingham-49538 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:13:07Z |
| publishDate | 2017 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-495382020-05-04T19:04:20Z https://eprints.nottingham.ac.uk/49538/ An experimental study of hyper-heuristic selection and acceptance mechanism for combinatorial t-way test suite generation Zamil, Kamal Z. Din, Fakhrud Kendall, Graham Ahmed, Bestoun S. 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, metaheuristic algorithms that make up these strategies often require specific domain knowledge in order to allow effective tuning before good quality solutions can be obtained. Hyperheuristics 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. Elsevier 2017-08-31 Article PeerReviewed Zamil, Kamal Z., Din, Fakhrud, 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 Software testing ; t-way testing ; Hyper-heuristics ; Meta-heuristics ; Fuzzy Inference Selection https://www.sciencedirect.com/science/article/pii/S0020025517305820 doi:10.1016/j.ins.2017.03.007 doi:10.1016/j.ins.2017.03.007 |
| spellingShingle | Software testing ; t-way testing ; Hyper-heuristics ; Meta-heuristics ; Fuzzy Inference Selection Zamil, Kamal Z. Din, Fakhrud Kendall, Graham Ahmed, Bestoun S. An experimental study of hyper-heuristic selection and acceptance mechanism for combinatorial t-way test suite generation |
| title | An experimental study of hyper-heuristic selection and acceptance mechanism for combinatorial t-way test suite generation |
| title_full | An experimental study of hyper-heuristic selection and acceptance mechanism for combinatorial t-way test suite generation |
| title_fullStr | An experimental study of hyper-heuristic selection and acceptance mechanism for combinatorial t-way test suite generation |
| title_full_unstemmed | An experimental study of hyper-heuristic selection and acceptance mechanism for combinatorial t-way test suite generation |
| title_short | An experimental study of hyper-heuristic selection and acceptance mechanism for combinatorial t-way test suite generation |
| title_sort | experimental study of hyper-heuristic selection and acceptance mechanism for combinatorial t-way test suite generation |
| topic | Software testing ; t-way testing ; Hyper-heuristics ; Meta-heuristics ; Fuzzy Inference Selection |
| url | https://eprints.nottingham.ac.uk/49538/ https://eprints.nottingham.ac.uk/49538/ https://eprints.nottingham.ac.uk/49538/ |