Test automation brings the potential to reduce costs and human effort, but several aspects of software testing remain challenging to automate. One such example is automated performance testing to find performance breaking points. Current approaches to tackle automated generation of performance test cases mainly involve using source code or system model analysis or use-case-based techniques. However, source code and system models might not always be available at testing time. 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 testing framework. SaFReL learns the optimal policy to generate performance test cases through an initial learning phase, then reuses it during a transfer learning phase, while keeping the learning running and updating the policy in the long term. Through multiple experiments in a simulated performance testing setup, we demonstrate that our approach generates the target performance test cases for different programs more efficiently than a typical testing process and performs adaptively without access to source code and performance models.

测试自动化具有降低成本和减少人工的潜力，但是软件测试的多个方面仍然难以实现自动化。这样的示例之一是自动性能测试，以查找性能突破点。解决自动生成性能测试用例的当前方法主要涉及使用源代码或系统模型分析或基于用例的技术。但是，源代码和系统模型可能并不总是在测试时可用。另一方面，如果可以通过测试系统获悉针对测试过程中预期目标的最佳性能测试策略，则可以在没有高级性能模型的情况下进行自动化测试。此外，学习到的策略以后可以在测试中的类似软件系统中重复使用，从而提高测试效率。我们提出了SaFReL，这是一种基于模糊强化学习的自适应性能测试框架。SaFReL在初始学习阶段学习最佳策略以生成性能测试用例，然后在迁移学习阶段重用它，同时长期保持学习的运行和更新策略。通过模拟性能测试设置中的多次实验，我们证明了我们的方法比典型的测试过程更有效地生成了不同程序的目标性能测试用例，并且可以自适应地执行而无需访问源代码和性能模型。然后在转移学习阶段重用它，同时保持学习运行并长期更新策略。通过模拟性能测试设置中的多次实验，我们证明了我们的方法比典型的测试过程更有效地生成了不同程序的目标性能测试用例，并且可以自适应地执行而无需访问源代码和性能模型。然后在转移学习阶段重用它，同时保持学习运行并长期更新策略。通过模拟性能测试设置中的多次实验，我们证明了我们的方法比典型的测试过程更有效地生成了不同程序的目标性能测试用例，并且可以自适应地执行而无需访问源代码和性能模型。