An experimental study of hyper-heuristic selection and acceptance mechanism for combinatorial t-way test suite generation

Zamil, Kamal Z.; Din, Fakhrud; Kendall, Graham; Ahmed, Bestoun S.

Abstract

Recently, many meta-heuristic algorithms have been proposed to serve as the basis of a t -way test generation strategy (where t indicates the interaction strength) including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Simulated Annealing (SA), Cuckoo Search (CS), Particle Swarm Optimization (PSO), and Harmony Search (HS). Although useful, metaheuristic algorithms that make up these strategies often require specific domain knowledge in order to allow effective tuning before good quality solutions can be obtained. Hyperheuristics provide an alternative methodology to meta-heuristics which permit adaptive selection and/or generation of meta-heuristics automatically during the search process. This paper describes our experience with four hyper-heuristic selection and acceptance mechanisms namely Exponential Monte Carlo with counter (EMCQ), Choice Function (CF), Improvement Selection Rules (ISR), and newly developed Fuzzy Inference Selection (FIS),using the t -way test generation problem as a case study. Based on the experimental results, we offer insights on why each strategy differs in terms of its performance.

Citation

Zamil, K. Z., Din, F., Kendall, G., & Ahmed, B. S. (2017). An experimental study of hyper-heuristic selection and acceptance mechanism for combinatorial t-way test suite generation. Information Sciences, 399, https://doi.org/10.1016/j.ins.2017.03.007

• Journal Article Type: Article
• Acceptance Date: Mar 3, 2017
• Online Publication Date: Mar 6, 2017
• Publication Date: Aug 31, 2017
• Deposit Date: Feb 5, 2018
• Publicly Available Date: Feb 5, 2018
• Journal: Information Sciences
• Print ISSN: 0020-0255
• Electronic ISSN: 0020-0255
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 399
• DOI: https://doi.org/10.1016/j.ins.2017.03.007
• Keywords: Software testing ; t-way testing ; Hyper-heuristics ; Meta-heuristics ; Fuzzy Inference Selection
• Public URL: https://nottingham-repository.worktribe.com/output/880773
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0020025517305820