Applying particle swarm optimization to software testing

Evolutionary structural testing is an approach to automatically generating test cases that achieve high structural code coverage. It typically uses genetic algorithms (GAs) to search for relevant test cases. In recent investigations particle swarm optimization (PSO), an alternative search technique, often outperformed GAs when applied to various problems. This raises the question of how PSO competes with GAs in the context of evolutionary structural testing. In order to contribute to an answer to this question, we performed experiments with 25 small artificial test objects and 13 more complex industrial test objects taken from various development projects. The results show that PSO outperforms GAs for most code elements to be covered in terms of effectiveness and efficiency.