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Adopting A Particle Swarm-Based Test Generator Strategy For Variable-Strength And T-Way Testing

Recently, researchers have started to explore the use of Artificial Intelligence (AI)-based algorithms as t-way (where t indicates the interaction strength) and variable-strength testing strategies. Many AI-based strategies have been developed, such as Ant Colony, Simulated Annealing, Genetic Alg...

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Main Author: S. Ahmed, Bestoun
Format: Thesis
Language:English
Published: 2011
Subjects:
TK1-9971 Electrical engineering. Electronics. Nuclear engineering
Online Access:http://eprints.usm.my/46326/1/BESTOUN%20S.%20AHMED_HJ.pdf
http://eprints.usm.my/46326/
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spelling my.usm.eprints.46326 http://eprints.usm.my/46326/ Adopting A Particle Swarm-Based Test Generator Strategy For Variable-Strength And T-Way Testing S. Ahmed, Bestoun TK1-9971 Electrical engineering. Electronics. Nuclear engineering Recently, researchers have started to explore the use of Artificial Intelligence (AI)-based algorithms as t-way (where t indicates the interaction strength) and variable-strength testing strategies. Many AI-based strategies have been developed, such as Ant Colony, Simulated Annealing, Genetic Algorithm, and Tabu Search. Although useful, most existing AI-based strategies adopt complex search processes and require heavy computations. For this reason, existing AI-based strategies have been confined to small interaction strengths (i.e., t≤3) and small test configurations. Recent studies demonstrate the need to go up to t=6 in order to capture most faults. This thesis presents the design and implementation of a new interaction test generation strategy, known as the Particle Swarm-based Test Generator (PSTG), for generating t-way and variable-strength test suites. Unlike other existing AI-based strategies, the lightweight computation of the particle swarm search process enables PSTG to support high interaction strengths of up to t=6. The performance of PSTG is evaluated using several sets of benchmark experiments. Comparatively, PSTG consistently outperforms its AI counterparts and other existing strategies as far as the size of the test suite is concerned. Furthermore, the case study demonstrates the usefulness of PSTG for detecting faulty interactions of the input components. 2011-12 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/46326/1/BESTOUN%20S.%20AHMED_HJ.pdf S. Ahmed, Bestoun (2011) Adopting A Particle Swarm-Based Test Generator Strategy For Variable-Strength And T-Way Testing. PhD thesis, Universiti Sains Malaysia.
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
language English
topic TK1-9971 Electrical engineering. Electronics. Nuclear engineering
spellingShingle TK1-9971 Electrical engineering. Electronics. Nuclear engineering
S. Ahmed, Bestoun
Adopting A Particle Swarm-Based Test Generator Strategy For Variable-Strength And T-Way Testing
description Recently, researchers have started to explore the use of Artificial Intelligence (AI)-based algorithms as t-way (where t indicates the interaction strength) and variable-strength testing strategies. Many AI-based strategies have been developed, such as Ant Colony, Simulated Annealing, Genetic Algorithm, and Tabu Search. Although useful, most existing AI-based strategies adopt complex search processes and require heavy computations. For this reason, existing AI-based strategies have been confined to small interaction strengths (i.e., t≤3) and small test configurations. Recent studies demonstrate the need to go up to t=6 in order to capture most faults. This thesis presents the design and implementation of a new interaction test generation strategy, known as the Particle Swarm-based Test Generator (PSTG), for generating t-way and variable-strength test suites. Unlike other existing AI-based strategies, the lightweight computation of the particle swarm search process enables PSTG to support high interaction strengths of up to t=6. The performance of PSTG is evaluated using several sets of benchmark experiments. Comparatively, PSTG consistently outperforms its AI counterparts and other existing strategies as far as the size of the test suite is concerned. Furthermore, the case study demonstrates the usefulness of PSTG for detecting faulty interactions of the input components.
format Thesis
author S. Ahmed, Bestoun
author_facet S. Ahmed, Bestoun
author_sort S. Ahmed, Bestoun
title Adopting A Particle Swarm-Based Test Generator Strategy For Variable-Strength And T-Way Testing
title_short Adopting A Particle Swarm-Based Test Generator Strategy For Variable-Strength And T-Way Testing
title_full Adopting A Particle Swarm-Based Test Generator Strategy For Variable-Strength And T-Way Testing
title_fullStr Adopting A Particle Swarm-Based Test Generator Strategy For Variable-Strength And T-Way Testing
title_full_unstemmed Adopting A Particle Swarm-Based Test Generator Strategy For Variable-Strength And T-Way Testing
title_sort adopting a particle swarm-based test generator strategy for variable-strength and t-way testing
publishDate 2011
url http://eprints.usm.my/46326/1/BESTOUN%20S.%20AHMED_HJ.pdf
http://eprints.usm.my/46326/
_version_ 1662755769388367872
score 13.211869

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