Abstract:  Reinforcement learning has become research focus in the field of machine learning in recent years due to its characteristic of generating dynamic data through interaction with the environment without requiring a large number of samples for training. This paper proposes a new software testing framework STRL based on reinforcement learning， which can effectively solve the problem of long time consuming and low state coverage of regression testing. STRL utilizes reinforcement learning algorithm PPO to achieve efficient adaptive exploration. Experiments results show that the STRL algorithm outperforms manual testing and automated script testing in terms of state coverage and testing time.

Key words: artificial intelligence software, intelligent software, traditional software, software lifecycle, reinforcement learning