In this paper a method of adaptive selection of helper-objectives in evolutionary algorithms, which was previously applied to model problems only, is applied to generation of test cases for programming challenge tasks. The method is based on reinforcement learning. Experiments show that the proposed method performs equally well compared to the best helper-objectives selected by hand.