An Autonomous Performance Testing Framework using Self-Adaptive Fuzzy Reinforcement Learning

TL;DR

SaFReL is an autonomous, self-adaptive fuzzy reinforcement learning framework that efficiently generates performance test cases without source code or models, learning and transferring policies over time.

Contribution

It introduces SaFReL, a novel reinforcement learning-based approach for automated performance testing that adapts and transfers policies without relying on source code or system models.

Findings

Outperforms traditional testing in efficiency

Adapts to different programs without source code

Continuously updates testing policies

Abstract

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 on a simulated environment, 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.