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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| 主要作者: | |
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| 格式: | Thesis |
| 语言: | English |
| 出版: |
2011
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| 主题: | |
| 在线阅读: | http://eprints.usm.my/46326/1/BESTOUN%20S.%20AHMED_HJ.pdf |
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| 总结: | 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. |
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