Extended finite state machines-based testing using metaheuristic search-based techniques: Issues, and open challenges

Extended finite state machines (EFSM) models are currently widely utilized to model embedded and control systems. Thus, there are recent upsurge studies to generate test data from EFSM models (EFSM-based testing). Applying metaheuristic search-based techniques (MHSBT) for automating the process of test case generation from EFSM models has become a proliferated field of interest during the last decade.lt has been defined that MHSBT find the optimal set of test cases among all possible test cases at reasonable cost. However, successful futuristic MHSBT for EFSM-based testing demand deep insight into the existing solutions that underlines stringent issues and challenges, which are lacking. The objective of this study is to analyze the current state-of-the-art of the application of MHSBT for EFSM-based testing. The study investigates the main issues in EFSM-based testing, including cost, continuous data, infeasible path, complex data structure, and concurrency. The current applications of MHSBT to solve these issues were elucidated. This study advocates that the majority of problems stem from the intrinsic features of EFSM models. Several open issues on EFSM-based testing adoption are presented as future research directions.