adapting swarm intelligence based methods for test data generation

To achieve highquality software, different tasks such as testing should be performed. testing is known as a complex and timeconsuming 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 timeefficient ways have presented different types of methods. evolutionary and swarmbased methods are among them. this work is aimed to study the applicability of swarmbased 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 builtin geneticbased methods in evosuite. the results show that swarmbased methods are successful in solving this problem and competitive results are obtained in comparison with the evolutionary methods.

Adapting Swarm Intelligence Based Methods for Test Data Generation

To achieve highquality software, different tasks such as testing should be performed. Testing is known as a complex and timeconsuming 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 timeefficient ways have presented different types of methods. Evolutionary and swarmbased methods are among them. This work is aimed to study the applicability of swarmbased 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 builtin geneticbased methods in EvoSuite. The results show that swarmbased methods are successful in solving this problem and competitive results are obtained in comparison with the evolutionary methods.