On the Influence of the Number of Objectives in Evolutionary Autonomous Software Agent Testing

Autonomous software agents are increasingly used in a wide range of applications. Thus, testing these entities is extremely crucial. However, testing autonomous agents is still a hard task since they may react in different manners for the same input over time. To address this problem, Nguyen et al. [6] have introduced the first approach that uses evolutionary optimization to search for challenging test cases. In this paper, we extend this work by studying experimentally the effect of the number of objectives on the obtained test cases. This is achieved by proposing five additional objectives and solving the new obtained problem by means of a Preference-based Many-Objective Evolutionary Testing (P-MOET) method. The obtained results show that the hardness of test cases increases with the rise of the number of objectives.