We consider the automatic online synthesis of black-box test cases from functional requirements specified as automata for reactive implementations. The goal of the tester is to reach some given state, so as to satisfy a coverage criterion, while monitoring the violation of the requirements. We develop an approach based on Monte Carlo Tree Search, which is a classical technique in reinforcement learning for efficiently selecting promising inputs. Seeing the automata requirements as a game between the implementation and the tester, we develop a heuristic by biasing the search towards inputs that are promising in this game. We experimentally show that our heuristic accelerates the convergence of the Monte Carlo Tree Search algorithm, thus improving the performance of testing.

翻译：我们研究从以自动机形式描述的功能需求中自动在线生成黑盒测试用例，用于反应式实现。测试者的目标是达到特定状态以满足覆盖准则，同时监测需求违规情况。我们提出了一种基于蒙特卡洛树搜索的方法，该方法是强化学习中用于高效选择有前景输入的经典技术。通过将自动机需求视为实现与测试者之间的博弈，我们开发了一种启发式方法，将搜索偏向于在该博弈中具有潜力的输入。实验结果表明，我们的启发式方法加速了蒙特卡洛树搜索算法的收敛，从而提升了测试性能。