An Experimental Study of Hyper-heuristic Selection and Acceptance Mechanism for Combinatorial T-Way Test Suite Generation

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 Optimizati...

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Main Authors: Kamal Z., Zamli, Fakhrud, Din, Kendall, Graham, Ahmed, Bestoun S.
Format: Article
Language:English
Published: Elsevier Ltd 2017
Subjects:
QA76 Computer software
Online Access:http://umpir.ump.edu.my/id/eprint/14604/
http://umpir.ump.edu.my/id/eprint/14604/
http://umpir.ump.edu.my/id/eprint/14604/
http://umpir.ump.edu.my/id/eprint/14604/2/An%20experimental%20study%20of%20hyper-heuristic%20selection%20and%20acceptance%20mechanism%20for%20combinatorial%20t-way%20test%20suite%20generation%201.pdf
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spelling ump-146042018-01-15T07:00:35Z http://umpir.ump.edu.my/id/eprint/14604/ An Experimental Study of Hyper-heuristic Selection and Acceptance Mechanism for Combinatorial T-Way Test Suite Generation Kamal Z., Zamli Fakhrud, Din Kendall, Graham Ahmed, Bestoun S. QA76 Computer software 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. Elsevier Ltd 2017 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/14604/2/An%20experimental%20study%20of%20hyper-heuristic%20selection%20and%20acceptance%20mechanism%20for%20combinatorial%20t-way%20test%20suite%20generation%201.pdf 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 http://doi.org/10.1016/j.ins.2017.03.007 DOI: 10.1016/j.ins.2017.03.007
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic QA76 Computer software
spellingShingle QA76 Computer software
Kamal Z., Zamli
Fakhrud, Din
Kendall, Graham
Ahmed, Bestoun S.
An Experimental Study of Hyper-heuristic Selection and Acceptance Mechanism for Combinatorial T-Way Test Suite Generation
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, 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.
format Article
author Kamal Z., Zamli
Fakhrud, Din
Kendall, Graham
Ahmed, Bestoun S.
author_facet Kamal Z., Zamli
Fakhrud, Din
Kendall, Graham
Ahmed, Bestoun S.
author_sort Kamal Z., Zamli
title 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_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_sort experimental study of hyper-heuristic selection and acceptance mechanism for combinatorial t-way test suite generation
publisher Elsevier Ltd
publishDate 2017
url http://umpir.ump.edu.my/id/eprint/14604/
http://umpir.ump.edu.my/id/eprint/14604/
http://umpir.ump.edu.my/id/eprint/14604/
http://umpir.ump.edu.my/id/eprint/14604/2/An%20experimental%20study%20of%20hyper-heuristic%20selection%20and%20acceptance%20mechanism%20for%20combinatorial%20t-way%20test%20suite%20generation%201.pdf
first_indexed 2023-09-18T22:18:33Z
last_indexed 2023-09-18T22:18:33Z
_version_ 1777415501878657024

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