Based on Deep Reinforcement Learning for Software Test Automation

Abstract

This paper explores the application of Deep Reinforcement Learning (DRL) to automate software testing. Traditional software testing methodologies heavily rely on predefined rules and templates, often proving inadequate against the complexity and variability of modern software systems. This research proposes a novel approach leveraging DRL to intelligently generate test cases and execute test procedures automatically. The core mechanism involves training a deep reinforcement learning agent to learn optimal testing strategies and methods. The agent learns through trial and error, maximizing reward based on test execution outcomes. We present a framework for implementing this approach, focusing on the challenges and potential benefits of automating the testing process with a learning agent. The results suggest that DRL can significantly improve the efficiency and effectiveness of software testing, particularly in scenarios with high complexity and evolving requirements. This work contributes to the growing field of intelligent software testing and offers a promising avenue for reducing testing costs and improving software quality.