Modern software applications demand efficient and reliable testing methodologies to ensure robust user interface functionality. This paper introduces an autonomous reinforcement learning (RL) agent integrated within a Behavior-Driven Development (BDD) framework to enhance UI testing. By leveraging the adaptive decision-making capabilities of RL, the proposed approach dynamically generates and refines test scenarios aligned with specific business expectations and actual user behavior. A novel system architecture is presented, detailing the state representation, action space, and reward mechanisms that guide the autonomous exploration of UI states. Experimental evaluations on open-source web applications demonstrate significant improvements in defect detection, test coverage, and a reduction in manual testing efforts. This study establishes a foundation for integrating advanced RL techniques with BDD practices, aiming to transform software quality assurance and streamline continuous testing processes.

翻译：现代软件应用需要高效可靠的测试方法以确保稳健的用户界面功能。本文介绍了一种集成于行为驱动开发（BDD）框架中的自主强化学习（RL）代理，以增强UI测试。该方法利用强化学习的自适应决策能力，动态生成并优化符合特定业务预期和实际用户行为的测试场景。本文提出了一种新颖的系统架构，详细阐述了指导UI状态自主探索的状态表示、动作空间和奖励机制。在开源Web应用上的实验评估表明，该方法在缺陷检测、测试覆盖率方面有显著提升，并减少了人工测试工作量。本研究为将先进强化学习技术与BDD实践相结合奠定了基础，旨在变革软件质量保障流程并优化持续测试过程。