Evaluating Bees Algorithm for Sequence-based T-way Testing Test Data Generation

Zabil, Mohd Hazli Mohamed; Zamli, Kamal Z.; Lim, Kyuhong

doi:10.17485/ijst/2018/v11i4/121086

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…T-way testing based on the Bee Colony algorithm is also efficient in generating optimal test cases; it can even outperform many existing computational and AI-based t-way strategies in terms of constructing optimum test cases [27]. Bee Algorithm based t-way strategies are also implemented in sequence-based testing [28]. However, Hybrid Artificial Bee Colony (HABC) Algorithm has been effective for a hamming based t-way strategy targeting variable strength test set generation [29].…”

Section: Related Workmentioning

confidence: 99%

Construction of Prioritized T-Way Test Suite Using Bi-Objective Dragonfly Algorithm

2022

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Section: Related Workmentioning

confidence: 99%

Construction of Prioritized T-Way Test Suite Using Bi-Objective Dragonfly Algorithm

2022

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T-way Test Suite Generation Strategy based on Ant Colony Algorithm to Support T-way Variable Strength

Ramli

Othman

Hendradi

et al. 2021

J. Phys.: Conf. Ser.

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T-way test suite generation strategy based on Ant Colony algorithm (TTSGA) has been developed to support t-way variable strength testing which tackles exhaustive testing issues. It employs the ant colony optimization algorithm to generate near-optimal number of test suite size. Even though the test suite size is smaller than exhaustive testing, the strategy covers every possible combination of interacting parameters. The strategy has been evaluated by using benchmarked experiments. Results obtained were compared with other existing strategies that support variable strength. It was found that TTSGA produces comparable results with other existing strategies especially for higher strength configurations. Two non-parametric tests, which are Wilcoxon Rank and Friedman test, have been conducted to analyze the results statistically between TTSGA and HSS as only both strategies have complete experiments results. Although the results shows that there is no significant difference of test suite size among them, TTSGA is in the first rank in the Friedman test.

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Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

2022

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Metaheuristic algorithm is a very important area of research that continuously improve in solving optimization problems. Nature-inspired is one of the classifications of metaheuristic algorithm that are becoming more popular among researchers for the last decades. Nature-inspired metaheuristic algorithms contributes significantly to tackling many standing complex problems (such as combinatorial tway testing problem) and achieving optimal results. One challenge in this area is combinatorial explosion problem which always intended to find the most optimal final test suite that will cover all combinations of a given interaction strength. As such, test case generation is selected as the most active research area in combinatorial t-way testing as Non-deterministic Polynomial-time hardness (NP-hard). However, not all metaheuristics are effectively adopted in combinatorial t-way testing, some proved to be effective and thus have been popular tools selected for optimization whilst others are not adopted. This research paper outlines hundred and ten (110) outstanding nature-inspired metaheuristic algorithms for the last decades (2001 and 2021) such as Coronavirus Optimization Algorithm, Ebola Optimization Algorithm, Harmony Search, Tiki-Taka Algorithm, and so on. The purpose of this review is to revisit and carry out up-to-date review on these distinguished algorithms with their respective current state of use. This is to inspire future research in the field of combinatorial t-way testing for better optimization. Thus, we found that all metaheuristics has a simple structure to be adopted in different areas for becoming a more efficient in optimization. Finally, we suggested some future paths of investigation for researchers who are interested in the combinatorial t-way testing field to employ more of these algorithms by tuning their parameters setting to achieve an optimal solution.

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Evaluating Bees Algorithm for Sequence-based T-way Testing Test Data Generation

Cited by 3 publications

References 11 publications

Construction of Prioritized T-Way Test Suite Using Bi-Objective Dragonfly Algorithm

Construction of Prioritized T-Way Test Suite Using Bi-Objective Dragonfly Algorithm

T-way Test Suite Generation Strategy based on Ant Colony Algorithm to Support T-way Variable Strength

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

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