Combinatorial Black Hole Algorithm: A Metaheuristic Approach for Combinatorial Testing

Combinatorial Black Hole Algorithm: A Metaheuristic Approach for Combinatorial Testing
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© 2023 by IJETT Journal
Volume-71 Issue-4
Year of Publication : 2023
Author : Izrulfizal Saufihamizal Ibrahim, Rosziati Ibrahim, Mazidah Mat Rejab
DOI : 10.14445/22315381/IJETT-V71I4P203

How to Cite?

Izrulfizal Saufihamizal Ibrahim, Rosziati Ibrahim, Mazidah Mat Rejab, "Combinatorial Black Hole Algorithm: A Metaheuristic Approach for Combinatorial Testing," International Journal of Engineering Trends and Technology, vol. 71, no. 4, pp. 21-28, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I4P203

Abstract
Combinatorial Testing (CT) is a software testing technique that aims to identify defects in complex systems by covering as many combinations of input parameters as possible within a given time and resource constraint. The black hole algorithm (BHA) is a metaheuristic approach that has been used in multiple problems involving optimization. In this paper, a new approach called the Combinatorial Black Hole Algorithm (CBHA) is presented for CT that combines the strengths of CT and BHA. The effectiveness of this approach is demonstrated through experiments on a series of real-world computer programs. The findings indicate that the method is feasible in identifying defects with fewer test cases and in less time needed compared to the current technology in CT techniques. The approach can also handle larger and more complex systems more effectively. This study contributes to the software testing field with a way of providing a new and efficient approach for CT that practitioners and researchers can use.

Keywords
Black hole algorithm, CT, Metaheuristics, Test cases.

References
[1] Tzoref-Brill, R, “Advances in Combinatorial Testing,” Advances in Computers, vol. 112, pp. 79-134, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Huayao Wu et al., “Combinatorial Testing of RESTful APIs,” ACM/IEEE International Conference on Software Engineering (ICSE), 2020.
[Google Scholar] [Publisher Link]
[3] Tansel Dokeroglu et al., “A Survey on New Generation Metaheuristic Algorithms,” Computers & Industrial Engineering, vol. 137, p. 106040, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Bernardo Morales-Castañeda et al., “A Better Balance in Metaheuristic Algorithms: Does It Exist?,” Swarm and Evolutionary Computation, vol. 54, p. 100671, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Madhavi, D, “A White Box Testing Technique in Software Testing: Basis Path Testing,” Journal for Research, vol. 2, no. 4, 2016.
[Google Scholar] [Publisher Link]
[6] Ramanathan.L, and Ulaganathan.K, "Nature-Inspired Metaheuristic Optimization Technique-Migrating Bird’S Optimization in Industrial Scheduling Problem," SSRG International Journal of Industrial Engineering, vol. 1, no. 2, pp. 12-17, 2014.
[CrossRef] [Publisher Link]
[7] Laith Abualigah et al., “Black Hole Algorithm: A Comprehensive Survey,” Applied Intelligence, pp. 1-24, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Abdolreza Hatamlou, “Solving Travelling Salesman Problem Using Black Hole Algorithm,” Soft Computing, vol. 22, no. 24, pp. 8167-8175, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Rodrigo Olivares et al., “Using Black Hole Algorithm to Improve EEG-Based Emotion Recognition,” Computational Intelligence and Neuroscience, vol. 2018, p. 21, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Abdolreza Hatamlou, “Application of Black Hole Algorithm for Solving Knapsack Problems,” Computer and Knowledge Engineering, vol. 3, no. 1, pp. 117-122, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Seeven Amic, K. M. Sunjiv Soyjaudah, and Gianeshwar Ramsawock, "Fitness Landscape Analysis of Block Ciphers for Cryptanalysis Using Metaheuristics," International Journal of Engineering Trends and Technology, vol. 70, no. 6, pp. 257-271, 2022.
[CrossRef] [Publisher Link]
[12] Omar Sabah Mohammed, Adel Abo Al-Maged Sewisy, and Ahmed Ibrahim Taloba, “Solving Optimization Problems Using Hybrid Metaheuristics: Genetic Algorithm and Black Hole Algorithm,” 2020 2nd International Conference on Computer and Information Sciences (ICCIS), pp. 1-5, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Derya Yeliz Ulutaş, and Ayşe Tosun, “A Condition Coverage-Based Black Hole Inspired Meta-Heuristic for Test Data Generation,” CEUR Workshop Proceedings, pp. 70-78, 2021.
[Google Scholar]
[14] Mohd Zamri Zahir Ahmad et al., “A Self-Adapting Ant Colony Optimization Algorithm Using Fuzzy Logic (ACOF) for Combinatorial Test Suite Generation,” IOP Conference Series: Materials Science and Engineering, vol. 767, no. 1, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Ali Abdullah Hassan et al., “Combinatorial Test Suites Generation Strategy Utilizing the Whale Optimization Algorithm,” IEEE Access, vol. 8, pp. 192288-192303, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Hamsa Naji Nsaif Al-Sammarraie, and Dayang N. A. Jawawi, “Multiple Black Hole Inspired Meta-Heuristic Searching Optimization for CT,” IEEE Access, vol. 8, pp. 33406-33418, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Khin Maung Htay et al., “Gravitational Search Algorithm Based Strategy for Combinatorial T-Way Test Suite Generation,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 8, pp. 4860-4873, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[18] J M Altmemi et al., “Implementation of Hybrid Sine Cosine Algorithm for Input-Output CT,” International Conference on Applied Computing, 2021.
[Google Scholar]
[19] Ammar K Alazzawi, Helmi Md Rais, and Shuib Basri “Artificial Bee Colony Algorithm for T-Way Test Suite Generation,” 4th International Conference on Computer and Information Sciences (ICCOINS), pp. 1-6, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Abdullah B. Nasser et al., “T-Way Test Suite Generation Based on Hybrid Flower Pollination Algorithm and Hill Climbing,” 10th International Conference on Software and Computer Applications, pp. 244-250 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Abdullah B. Nasser, and Kamal Z. Zamli “A New Variable Strength T-Way Strategy Based on the Cuckoo Search Algorithm,” Intelligent and Interactive Computing, pp. 193-203, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Muhammad Afiq Ariffin et al., “Test Cases Prioritization Using Ant Colony Optimization and Firefly Algorithm,” International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 22–28, 2022.
[CrossRef] [Publisher Link]
[23] Deepak kumar, and Manu Phogat “Genetic Algorithm Approach for Test Case Generation Randomly: A Review,” International Journal of Computer Trends and Technology, vol. 49, no. 4, pp. 213-216, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Santosh Kumar, Deepanwita Datta, and Sanjay Kumar Singh, “Black Hole Algorithm and Its Applications,” Computational Intelligence Applications in Modelling and Control, pp. 147-170, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Hamsa N Nsaif, and Dayang Norhayati Abang Jawawi, “Binary Black Hole-Based Optimization for T-Way Testing,” IOP Conference Series: Materials Science and Engineering, vol. 864, no. 1, p. 012073.
[CrossRef] [Google Scholar] [Publisher Link]