On Adopting Parameter Free Optimization Algorithms for Combinatorial Interaction Testing

Combinatorial interaction testing is a practical approach aims to detect defects due to unwanted and faulty interactions. Here, a set of sampled test cases is generated based on t-way covering problem (where t indicates the interaction strength). Often, the generation process is based on a particula...

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Main Authors:	Kamal Z., Zamli, Alsariera, Yazan A., Nasser, Abdullah B., Alsewari, Abdulrahman A.
Format:	Article
Language:	English
Published:	Asian Research Publishing Network (ARPN) 2015
Subjects:	Q Science (General) T Technology (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/18126/ http://umpir.ump.edu.my/id/eprint/18126/ http://umpir.ump.edu.my/id/eprint/18126/1/04.%20ARPN1015_2825.pdf

id	ump-18126
recordtype	eprints
spelling	ump-181262018-03-19T05:29:14Z http://umpir.ump.edu.my/id/eprint/18126/ On Adopting Parameter Free Optimization Algorithms for Combinatorial Interaction Testing Kamal Z., Zamli Alsariera, Yazan A. Nasser, Abdullah B. Alsewari, Abdulrahman A. Q Science (General) T Technology (General) Combinatorial interaction testing is a practical approach aims to detect defects due to unwanted and faulty interactions. Here, a set of sampled test cases is generated based on t-way covering problem (where t indicates the interaction strength). Often, the generation process is based on a particular t-way strategy ensuring that each t-way interaction is covered at least once. Much useful progress has been achieved as plethora of t-way strategies have been developed in the literature in the last 30 years. Recently, in line with the upcoming field called Search based Software Engineering (SBSE), many newly strategies have been developed adopting specific optimization algorithm (e.g. Genetic Algorithm (GA), Ant Colony (AC), Simulated Annealling (SA), Particle Swarm Optimization, and Harmony Search Algorithm (HS) as their basis in an effort to generate the most optimal solution. Although useful, strategies based on the aforementioned optimization algorithms are not without limitation. Specifically, these algorithms require extensive tuning before optimal solution can be obtained. In many cases, improper tuning of specific parameters undesirably yields suboptimal solution. Addressing this issue, this paper proposes the adoption of parameter free optimization algorithms as the basis of future t-way strategies. In doing so, this paper reviews two existing parameter free optimization algorithms involving Teaching Learning Based Optimization (TLBO) and Fruitfly Optimization Algorithm (FOA) in an effort to promote their adoption for CIT. Asian Research Publishing Network (ARPN) 2015-10 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/18126/1/04.%20ARPN1015_2825.pdf Kamal Z., Zamli and Alsariera, Yazan A. and Nasser, Abdullah B. and Alsewari, Abdulrahman A. (2015) On Adopting Parameter Free Optimization Algorithms for Combinatorial Interaction Testing. Journal of Engineering and Applied Sciences, 10 (19). pp. 8987-8994. ISSN 1819-6608 http://www.arpnjournals.org/jeas/research_papers/rp_2015/jeas_1015_2825.pdf
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	Q Science (General) T Technology (General)
spellingShingle	Q Science (General) T Technology (General) Kamal Z., Zamli Alsariera, Yazan A. Nasser, Abdullah B. Alsewari, Abdulrahman A. On Adopting Parameter Free Optimization Algorithms for Combinatorial Interaction Testing
description	Combinatorial interaction testing is a practical approach aims to detect defects due to unwanted and faulty interactions. Here, a set of sampled test cases is generated based on t-way covering problem (where t indicates the interaction strength). Often, the generation process is based on a particular t-way strategy ensuring that each t-way interaction is covered at least once. Much useful progress has been achieved as plethora of t-way strategies have been developed in the literature in the last 30 years. Recently, in line with the upcoming field called Search based Software Engineering (SBSE), many newly strategies have been developed adopting specific optimization algorithm (e.g. Genetic Algorithm (GA), Ant Colony (AC), Simulated Annealling (SA), Particle Swarm Optimization, and Harmony Search Algorithm (HS) as their basis in an effort to generate the most optimal solution. Although useful, strategies based on the aforementioned optimization algorithms are not without limitation. Specifically, these algorithms require extensive tuning before optimal solution can be obtained. In many cases, improper tuning of specific parameters undesirably yields suboptimal solution. Addressing this issue, this paper proposes the adoption of parameter free optimization algorithms as the basis of future t-way strategies. In doing so, this paper reviews two existing parameter free optimization algorithms involving Teaching Learning Based Optimization (TLBO) and Fruitfly Optimization Algorithm (FOA) in an effort to promote their adoption for CIT.
format	Article
author	Kamal Z., Zamli Alsariera, Yazan A. Nasser, Abdullah B. Alsewari, Abdulrahman A.
author_facet	Kamal Z., Zamli Alsariera, Yazan A. Nasser, Abdullah B. Alsewari, Abdulrahman A.
author_sort	Kamal Z., Zamli
title	On Adopting Parameter Free Optimization Algorithms for Combinatorial Interaction Testing
title_short	On Adopting Parameter Free Optimization Algorithms for Combinatorial Interaction Testing
title_full	On Adopting Parameter Free Optimization Algorithms for Combinatorial Interaction Testing
title_fullStr	On Adopting Parameter Free Optimization Algorithms for Combinatorial Interaction Testing
title_full_unstemmed	On Adopting Parameter Free Optimization Algorithms for Combinatorial Interaction Testing
title_sort	on adopting parameter free optimization algorithms for combinatorial interaction testing
publisher	Asian Research Publishing Network (ARPN)
publishDate	2015
url	http://umpir.ump.edu.my/id/eprint/18126/ http://umpir.ump.edu.my/id/eprint/18126/ http://umpir.ump.edu.my/id/eprint/18126/1/04.%20ARPN1015_2825.pdf
first_indexed	2023-09-18T22:25:29Z
last_indexed	2023-09-18T22:25:29Z
_version_	1777415937768554496

On Adopting Parameter Free Optimization Algorithms for Combinatorial Interaction Testing

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