 In this paper, we propose a self-adaptive fuzzy reinforcement learning-based performance (stress) testing framework (SaFReL) that enables the tester agent to learn the optimal policy for generating stress test cases leading to performance breaking point without access to performance model of the system under test. SaFReL learns the optimal policy  through an initial learning  , then reuses it during a transfer learning phase, while keeping the learning running in the long-term .
<coherence>  In this paper, we propose a self-adaptive fuzzy reinforcement learning-based performance (stress) testing framework (SaFReL) that enables the tester agent to learn the optimal policy for generating stress test cases leading to performance breaking point without access to performance model of the system under test. SaFReL learns the optimal policy  through an initial learning  , then reuses it during a transfer learning phase, while keeping the learning running in the long-term .
 In this paper, we propose a self-adaptive fuzzy reinforcement learning-based performance testing framework. SaFReL learns the optimal policy through an initial learning , then reuses it during a transfer learning phase, while keeping the learning running in the long-term .