Metaheuristic-Based Test Case Selection for Regression Testing: A Systematic Literature Review

Abstract

Regression testing plays a crucial role in ensuring that software modifications do not adversely affect existing functionality. However, test suites continue to grow, and the retest-all method has become increasingly impractical due to high execution costs and time constraints. Consequently, Test Case Selection (TCS) has emerged as an important optimization method to identify which test cases are relevant for re-execution. Metaheuristic optimization has received a great deal of attention in Search-Based Software Testing (SBST) for balancing the conflicting objectives of cost reduction and fault detection effectiveness. However, there are still multiple areas of fragmentation within the research, including algorithm design, objective formulation, empirical evaluation, and reporting practices. Results indicate that evolutionary algorithms and swarm intelligence are the primary algorithms used for TCS, with an increasing trend towards hybrid and multi-objective approaches to increase the quality and scalability of the search. Multi-objective formulations are significant in this context, as such approaches provide a structured mechanism to capture the trade-off between testing cost and effectiveness through Pareto-based evaluations. Empirical evidence remains largely dependent on benchmark and open-source systems, although recent studies increasingly incorporate industrial and Artificial Intelligence (AI)-based environments. Overall, the findings indicate that there is a lack of single techniques that are universally superior, as performance is strongly influenced by system characteristics and evaluation criteria. Future research should emphasize standardized evaluation practices, stronger industry-scale validation, and the development of domain-aware TCS techniques to improve practical applicability.