An Autonomous RL Agent Methodology for Dynamic Web UI Testing in a BDD Framework
By: Ali Hassaan Mughal
Potential Business Impact:
Tests apps better by learning how people use them.
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.
Similar Papers
Deep Reinforcement Learning for Automated Web GUI Testing
Software Engineering
Finds hidden bugs in websites automatically.
Integrating Human Feedback into a Reinforcement Learning-Based Framework for Adaptive User Interfaces
Human-Computer Interaction
Makes apps learn how you like them.
Adaptive Reinforcement Learning for Dynamic Configuration Allocation in Pre-Production Testing
Software Engineering
Tests software better by learning from mistakes.