Adapting Swarm Intelligence Based Methods for Test Data Generation

To achieve high-quality software, different tasks such as testing should be performed. Testing is known as a complex and time-consuming task. Efficient test suite generation (TSG) methods are required to suggest the best data for test designers to obtain better coverage in terms of testing criteria. In recent years, researchers to generate test data in time-efficient ways have presented different types of methods. Evolutionary and swarm-based methods are among them. This work is aimed to study the applicability of swarm-based methods for efficient test data generation in EvoSuite. The Firefly Algorithm (FA), Particle Swarm Optimization (PSO), Teaching Learning Based Optimization (TLBO), and Imperialist Competitive Algorithm (ICA) are used here. These methods are added to the EvoSuite. The methods are adapted to work in a discrete search space of test data generation problem. Also, a movement pattern is presented for generating new solutions. The performances of the presented methods are compared over 103 java classes with two built-in genetic-based methods in EvoSuite. The results show that swarm-based methods are successful in solving this problem and competitive results are obtained in comparison with the evolutionary methods.