 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.
<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.
 On the other hand, if the optimal performance testing policy for the intended objective in a testing process instead could be learned by the testing system, then test automation without advanced performance models could be possible. Furthermore, the learned policy could later be reused for similar software systems under test, thus leading to higher test efficiency. We propose SaFReL, 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.