Generating Test Data for Software Structural Testing Based on Particle Swarm Optimization

Abstract

Testing is an important way to ensure and improve the quality of software system. However, it is a time-consuming and labor-intensive activity. In the paper, our main concern is software structural testing, and propose a search-based test data generation solution. In our framework, particle swarm optimization (PSO) technique is adopted due to its simplicity and fast convergence speed. For test data generation problem, the inputs of program under test are encoded into particles. Once a set of test inputs is produced, coverage information can be collected by test driver. Meanwhile, the fitness value of branch coverage can be calculated based on such information. Then, the fitness is used for PSO to adjust the search direction. Finally, the test data set with the highest coverage rate is yielded. In addition, eight well-known programs are used for experimental analysis. The results show that PSO-based approach has a distinct advantage compared to the traditional evolutionary algorithms such as generic algorithm and simulated annealing, and also outperforms comprehensive learning-PSO both in covering effect and in evolution speed.