In the pursuit of software quality, testing plays a pivotal role in identifying and rectifying errors and defects before software is delivered to users. However, the increasing complexity of modern software, driven by...
详细信息
In the pursuit of software quality, testing plays a pivotal role in identifying and rectifying errors and defects before software is delivered to users. However, the increasing complexity of modern software, driven by diverse user needs, has resulted in numerous input functions and options. While exhaustive testing remains ideal, practical limitations in terms of time and cost impede exhaustive efforts. to address these challenges, researchers have turned to metaheuristic algorithms to formulate effective t-waytesting strategies, wherein 't' denotes parameter interaction strength. these strategies encompass a range of exploitation and exploration techniques for generating test data. Despite these advancements, challenges persist in test case generation, such as enhancing software quality through parameter seeding and managing combinatorial explosion by understanding parameter constraints among system elements. though existing metaheuristic-based t-way strategies offer valuable insights, none reign supreme over their counterparts. to bridge this gap, this article introduces a pioneering approach called SCHOP, integrating seeding and constraint supports within a harmony search algorithm by adopting a one-parameter-at-a-time approach. Benchmarking results illustrate the competitive performance of SCHOP across well-known benchmarking configurations against other strategies. SCHOP's benchmarking results showed success in 61.07% of cases, with 80 out of 131 entries performing well. However, there were 51 instances of suboptimal test suite sizes (38.93%). Further analysis revealed statistical significance in 12 out of 61 cases, with 48 out of 61 cases showing no significant difference, all at a 95% confidence level ( $\alpha =0.05$ ). Finally, SCHOP concurrently integrates seeding and constraint support within the harmony search algorithm to boost software quality while reducing the test suite's size.
the metaheuristic algorithm is a very important area of research that continuously improves in solving optimization problems. Nature-inspired is one of the metaheuristic algorithm classifications that has grown in pop...
详细信息
the metaheuristic algorithm is a very important area of research that continuously improves in solving optimization problems. Nature-inspired is one of the metaheuristic algorithm classifications that has grown in popularity among researchers over the last few decades. Nature-inspired metaheuristic algorithms contribute significantly to tackling many standing complex problems (such as the combinatorial t-way testing problem) and achieving optimal results. One challenge in this area is the combinatorial explosion problem, which is 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 has been 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 were not. this research paper outlines a hundred and ten (110) outstanding nature-inspired metaheuristic algorithms for the last decades (2001 and 2021), such as the 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 an up-to-date review of these distinguished algorithms with their respective current states 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 have a simple structure that can be adopted in different areas to become 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 settings to achieve an optimal solution.
暂无评论