Whale Optimization Algorithm Strategies for Higher Interaction Strength T-Way Testing

Abstract

Much of our daily tasks have been computerized by machines and sensors communicating with each other in real-time. There is a reasonable risk that something could go wrong because there are a lot of sensors producing a lot of data. Combinatorial testing (CT) can be used in this case to reduce risks and ensure conformance to specifications. Numerous existing meta-heuristic-based solutions aim to assist the test suite generation for combinatorial testing, also known as t-way testing (where t indicates the interaction strength), viewed as an optimization problem. Much previous research, while helpful, only investigated a small number of interaction strengths up to t = 6. For lightweight applications, research has demonstrated good fault-finding ability. However, the number of interaction strengths considered must be higher in the case of interactions that generate large amounts of data. Due to resource restrictions and the combinatorial explosion challenge, little work has been done to produce high-order interaction strength. In this context, the Whale Optimization Algorithm (WOA) is proposed to generate high-order interaction strength. To ensure that WOA conquers premature convergence and avoids local optima for large search spaces (owing to high-order interaction), three variants of WOA have been developed, namely Structurally Modified Whale Optimization Algorithm (SWOA), Tolerance Whale Optimization Algorithm (TWOA), and Tolerance Structurally Modified Whale Optimization Algorithm (TSWOA). Our experiments show that the third strategy gives the best performance and is comparable to existing state-of-the-arts based strategies.