Parameter Setting in Ant Colony-Based Software Testing

Abstract

Ant Colony Optimization (ACO) has been widely applied in search-based software testing (SBST) for tasks such as test case generation, test suite optimization, and fault detection. However, the effectiveness of ACO in these applications is highly dependent on parameter tuning, which directly influences convergence, efficiency, and reliability. This study investigates the parameter settings used in ACO-based software testing by systematically reviewing existing research. The analysis identifies the most used parameter values and examines their impact on testing performance. Results indicate that parameters such as pheromone evaporation rate, heuristic weight, and the number of ants significantly influence the algorithm’s effectiveness, with a balanced trade-off between pheromone influence and heuristic information being a prevalent approach. The findings provide insights into optimizing ACO parameterization for software testing and suggest future research directions for adaptive tuning mechanisms to enhance its efficiency.