Reinforcement Learning for Optimizing Test Case Execution in Automated Testing

Abstract

This study explores the use of reinforcement learning (RL) techniques to optimize test case execution in automated testing frameworks, addressing the inefficiencies of traditional testing methods. The primary research problem involves enhancing testing efficiency, improving coverage, and reducing redundancy through intelligent RL-based optimization. The study employed a design that integrated RL algorithms into automated testing frameworks, involving the development and training of an RL model, followed by empirical evaluation. ajor findings indicate that RL-based optimization significantly reduced test case execution time, improved test coverage, and minimized redundancy compared to conventional methods. The RL model dynamically adjusted test case sequences based on real-time feedback, leading to enhanced efficiency and more comprehensive testing. The study concludes that RL techniques offer a promising approach to overcoming traditional testing limitations, demonstrating tangible benefits in real-world scenarios. To put in a nutshell, RL-based optimization effectively addresses key challenges in automated testing, offering a more adaptive and efficient strategy for test case execution.