Combinatorial Test Suite Generation Strategy Using Enhanced Sine Cosine Algorithm

Zamli, Kamal Z.; Din, Fakhrud; Nasser, Abdullah B.; Alsewari, AbdulRahman A.

doi:10.1007/978-981-15-2317-5_12

Cited by 5 publications

(6 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since its proposed, it has received widespread attention and been applied in various optimization like routing problem [296], feature selection problem [297], and so on. Moreover, SCA metaheuristic has been introduced around t-way testing [298] [299] for combinatorial optimization problem.…”

Section: Physic-based Techniquementioning

confidence: 99%

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

2022

View full text Add to dashboard Cite

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.

show abstract

Section: Physic-based Techniquementioning

confidence: 99%

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

2022

View full text Add to dashboard Cite

show abstract

“…It was demonstrated that the effectiveness of the adaptive versions compared to original SCA in terms of accuracy and convergence speed. Zamli et al (2020) proposed an adaptive SCA for solving the combinatorial testing problem. A combination of linear and exponential magnitude update strategies was used for the search displacement.…”

Section: Chaotic Sine Cosine Algorithmmentioning

confidence: 99%

A comprehensive survey of sine cosine algorithm: variants and applications

et al. 2021

View full text Add to dashboard Cite

Sine Cosine Algorithm (SCA) is a recent meta-heuristic algorithm inspired by the proprieties of trigonometric sine and cosine functions. Since its introduction by Mirjalili in 2016, SCA has attracted great attention from researchers and has been widely used to solve different optimization problems in several fields. This attention is due to its reasonable execution time, good convergence acceleration rate, and high efficiency compared to several well-regarded optimization algorithms available in the literature. This paper presents a brief overview of the basic SCA and its variants divided into modified, multi-objective, and hybridized versions. Furthermore, the applications of SCA in several domains such as classification, image processing, robot path planning, scheduling, radial distribution networks, and other engineering problems are described. Finally, the paper recommended some potential future research directions for SCA.

show abstract

“…Combinatorial interaction testing strategies use Greedy test suite construction algorithms [17]. Every iteration of the design process aims to cover the maximum number of combinations.…”

Section: Related Workmentioning

confidence: 99%

“…• Sine Cosine Algorithm (SCA) [17] • Multiple Black Hole (MBH) algorithm [39] Cuckoo Search (CS) [4], which was implemented in t-way combinatorial testing with a small number of control parameters. A variant of CS was also implemented in t-way testing that upgrades the search space with Levi flights [31].…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, MBH introduced the black hole energy to promote the removal of certain black hole swarms and to produce fresh ones [39]. Another algorithm introduced in 2020 is SCA [17], which is a population-based algorithm that produces numerous initial random test cases and allows the cases to fluctuate outwards or towards the best possible test case using a sine and cosine mathematical model. SCA was enhanced by introducing a combination of linear and exponential magnitude updates for search displacement [17].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Combinatorial Test Suites Generation Strategy Utilizing the Whale Optimization Algorithm

et al. 2020

Self Cite

View full text Add to dashboard Cite

The potentially many software system input combinations make exhaustive testing practically impossible. To address this issue, combinatorial t-way testing (where t indicates the interaction strength, i.e. the number of interacting parameters (input)) was adopted to minimize the number of cases for testing. Complimentary to existing testing techniques (e.g. boundary value, equivalence partitioning, cause and effect graphing), combinatorial testing helps to detect faults caused by the faulty interaction between input parameters. In the last 15 years, applications of meta-heuristics as the backbone of t-way test suite generation have shown promising results (e.g. Particle Swarm Optimization, Cuckoo Search, Flower Pollination Algorithm, and Hyper-Heuristics (HHH), to name a few). Supporting the new free lunch theorem, as well as potentially offering new insights into the whole process of t-way generation, this paper proposes a new strategy with constraint support based on the Whale Optimization Algorithm (WOA). Our work is the first attempt to adopt the WOA as part of a search-based software engineering (SBSE) initiative for t-way test suite generation with constraint support. The experimental results of the test-suite generation indicate that WOA produces competitive outcomes compared to some selected single-based and population-based metaheuristic algorithms.

show abstract

Combinatorial Test Suite Generation Strategy Using Enhanced Sine Cosine Algorithm

Cited by 5 publications

References 26 publications

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

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

A comprehensive survey of sine cosine algorithm: variants and applications

Combinatorial Test Suites Generation Strategy Utilizing the Whale Optimization Algorithm

Contact Info

Product

Resources

About