Generation of software test cases using hybridization of grey wolf optimizer and artificial bee colony algorithm

Automated test case generation (ATCG) is a crucial task in the process of software testing. It aims to reduce the overall time required for manual testing and minimize total overhead. Evolutionary algorithms (EAs) in software testing are problem-specific applications explored by a few researchers but remain underutilized. This paper designs a local search strategy by hybridizing two search-based algorithms (SBAs): the Artificial Bee Colony (ABC) algorithm and the Grey Wolf Optimizer (GWO) algorithm; inspired by domain-specific knowledge. The proposed algorithm is constructed by integrating the duty-based phases of these two algorithms and merging them to form subsequent levels of the hybrid algorithm. The proposed algorithm is evaluated using three specific performance metrics: percentage coverage, test suite size, and time taken. Experimental results demonstrate enhanced performance compared to both its constituent algorithms and the conventional baseline algorithms, including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).