An Empirical Evaluation of Test Data Generation Techniques

Abstract

Software testing cost can be reduced if the process of testing is automated. However, the test data generation task is still performed mostly by hand although numerous theoretical works have been proposed to automate the process of generating test data and even commercial test data generators appeared on the market. Despite prolific research reports, few attempts have been made to evaluate and characterize those techniques. Therefore, a lot of works have been proposed to automate the process of generating test data. However, there is no overall evaluation and comparison of these techniques. Evaluation and comparison of existing techniques are useful for choosing appropriate approaches for particular applications, and also provide insights into the strengths and weaknesses of current methods. This paper conducts experiments on four representative test data generation techniques and discusses the experimental results. The results of the experiments show that the genetic algorithm (GA)-based test data generation performs the best. However, there are still some weaknesses in the GA-based method. Therefore, we modify the standard GA-based method to cope with these weaknesses. The experiments are carried out to compare the standard GA-based method and two modified versions of the GA-based method.