Application of Particle Swarm Optimization to uniform and variable strength covering array construction

Ahmed, Bestoun S.; Zamli, Kamal Z.; Lim, Chee Peng

doi:10.1016/j.asoc.2011.11.029

Cited by 89 publications

(82 citation statements)

References 38 publications

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“…The approaches that have generated the best results using artificial intelligence are particle swarm optimization (PSO), simulated annealing (SA), and tabu search (TS). Ahmed et al proposed Particle Swarm-based t-way Test Generator (PSTG) [22] to solve instances where t 2 f2 À 6g. PSTG generates test cases at random and selects the one that covers more interactions using PSO in a greedy fashion with the aim of identifying the better particles (test cases).…”

Section: Related Workmentioning

confidence: 99%

New bounds for mixed covering arrays in t-way testing with uniform strength

Gonzalez-Hernandez

2015

Information and Software Technology

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Section: Related Workmentioning

confidence: 99%

New bounds for mixed covering arrays in t-way testing with uniform strength

Gonzalez-Hernandez

2015

Information and Software Technology

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“…This is the basic search strategy of HC. Instead of moving Evolve SA [12,13,14,15,17,30,33,34] Test Set TS [15,21,28,37] GA [19,22] HC, SA [7,27] Evolve TS [7] Test Case GA [27,31,35] ACO [9,27,31] PSO [1,2,3,10,27] to better points only, SA can accept a worse move with a probability controlled by a cooling schedule to avoid local optimum. In addition, TS apply another strategy that prevents making a transformation if it has been made during the last several moves.…”

Section: Search Techniquesmentioning

confidence: 99%

An overview of search based combinatorial testing

Nie

2014

Proceedings of the 7th International Workshop on Search-Based Software Testing

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Combinatorial testing (CT) is a branch of software testing, which aims to detect the interaction triggered failures as much as possible. Search based combinatorial testing is to use the search techniques to solve the problem in combinatorial testing. It has been shown to be effective and promising. In this paper, we aim to provide an overview of search based combinatorial testing, especially focusing on test suite generation without constraint, and discuss the potential future directions in this field.

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“…We compare HHH against existing strategies based on the benchmark experiments in terms of test sizes as defined in [9,11,56,[63][64][65][66][67][68]. Considering that the test sizes are absolute and not affected by the computational platform, our comparison spans many t-way strategies from meta-heuristic-based to general computation ones.…”

Section: Benchmarking With Existing Strategies On Test Sizesmentioning

confidence: 99%

A Tabu Search hyper-heuristic strategy for t-way test suite generation

Zamli

Alkazemi

Kendall

2016

Applied Soft Computing

Self Cite

104

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This paper proposes a novel hybrid t-way test generation strategy (where t indicates interaction strength), called High Level Hyper-Heuristic (HHH). HHH adopts Tabu Search as its high level meta-heuristic and leverages on the strength of four low level meta-heuristics, comprising of Teaching Learning Based Optimization, Global Neighborhood Algorithm, Particle Swarm Optimization, and Cuckoo Search Algorithm. HHH is able to capitalize on the strengths and limit the deficiencies of each individual algorithm in a collective and synergistic manner. Unlike existing hyper-heuristics, HHH relies on three defined operators, based on improvement, intensification and diversification, to adaptively select the most suitable meta-heuristic at any particular time. Our results are promising as HHH manages to outperform existing t-way strategies on many of the benchmarks.

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Application of Particle Swarm Optimization to uniform and variable strength covering array construction

Cited by 89 publications

References 38 publications

New bounds for mixed covering arrays in t-way testing with uniform strength

New bounds for mixed covering arrays in t-way testing with uniform strength

An overview of search based combinatorial testing

A Tabu Search hyper-heuristic strategy for t-way test suite generation

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