Implementation of the Test Data Generation Algorithm Based on the Ant Colony Optimization Pheromone Model

In present paper we investigate an approach to intelligent support of the software white box testing process based on evolutionary paradigm. As a part of this approach, we solve the urgent problem of generating the optimal set of test data that provides maximum statement coverage of the code when it is used in the testing process. Earlier approaches that have been explored have shown the need to adjust the value of k for different programs, since its value has a significant impact on the quality of the fitted test data. To eliminate this problem, we propose to use the pheromone model, which is used in Ant Colony Optimizations in order to shift the focus of data generation to unexplored paths.