Test Data Generation Using Optimization Algorithm: An Empirical Evaluation

Abstract

This paper aims to design an approach for making an efficient fitness function for tests case generation based on distance from the goals. The designed function is given as an input to the genetic algorithm, and the result of the search process using the formed fitness function is evaluated in terms of time and the average number of test cases generated. This paper also investigates the effect of parameter setting such as the size of the initial population on the performance of genetic algorithm using the proposed fitness function. The experimental result shows that the proposed approach is both time and cost efficient in comparison with manual and random testing. It is also found that initial larger population size gives better results in comparison with low initial population.