An Autonomous RL Agent Methodology for Dynamic Web UI Testing in a BDD Framework

• URL: http://arxiv.org/abs/2503.08464v2
• Date: Wed, 12 Mar 2025 01:31:03 GMT
• Title: An Autonomous RL Agent Methodology for Dynamic Web UI Testing in a BDD Framework
• Abstract summary: This paper introduces an autonomous reinforcement learning (RL) agent integrated within a Behavior-Driven Development (BDD) framework to enhance user interface testing.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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