Abstract: Software testing has always been an essential pillar in ensuring software reliability and satisfaction of user requirements. Software systems are complex and require thorough testing to improve reliability and quality. However, manual test case design has a notorious history of being time-consuming and is subject to human error. Even most available automated methods are inflexible and require significant time, effort, and financial resources. Recently, search-based test data generation has become a significant, effective, and practical approach to overcoming these obstacles, and many meta-heuristic algorithms have been proposed to generate test cases to achieve branch coverage. Even though these strategies have shown good performance, researchers can further optimize these approaches. This paper proposes an automated test-case generation and optimization model that integrates activity diagram modelling with a Hybrid Adaptive Bat Particle Swarm Algorithm (ABPSA). Activity diagrams are used to represent the system's dynamic behavior, while the ABPSA is a synergistic combination of the exploratory nature of the Bat algorithm and the adaptive optimization of Particle Swarm Optimization. The algorithm is aimed at dynamically tracking the development of the activity-diagram model and thus effectively producing optimized test data. The effectiveness of the given framework is empirically demonstrated through a case study of the ATM withdrawal process.

Key words: test case generation, optimization, activity diagram, heuristic algorithms, adaptive bat particle swarm algorithm