Abstract
Intelligent agents consist in a promising computing technology for the development of complex distributed systems. Despite the available theoretical references for guiding the designer of these agents, there are few proposed testing techniques to validate these systems. It's known that this validation depends on all the selected test cases, which should provide information regarding the components in the structure of the agent that show unsatisfactory performance. This article presents the application of Artificial Immune Systems (AIS), through Clonal Selection Algorithm (CLONALG), for the problem of optimization of selection of test cases for testing computing systems that are based on intelligent agents. In order to validate the use of CLONALG, comparisons between the Genetic Algorithms (GA) and Ant Colony Optimization Algorithms (ACO) techniques were performed. In the experiments with the approach testing intelligent agents with different types of architecture in partially and completely observable environments, the approach selected a group of satisfactory test cases in terms of the generated information about the irregular performance of the agent. From this result, the approach enables the identification of problematic episodes, allowing the designer to make objective changes in the internal structure of the agent in such a way to improve its performance.