Genetic Algorithms and Its Application in Software Test Data Generation

Abstract

Test data generation is a key part in software test area and it is of significance to realize the automation of software testing. The main contribution of this paper lies in that a practical model, which utilizes genetic algorithms as searching policy to generate software structural test data, is proposed. To achieve higher performance, such issues as encoding strategy, algorithms operator evolution, evaluation function construction and instrumentation are addressed in detail, a new method of initialization of population is introduced in order to make the initial population has higher adaptability, and much emphasis is put on algorithms operator evolution, which is a key factor which can highly affect algorithms efficiency, finally, the results show that the application of genetic algorithms in software test data generation is more efficient compared with other methods.