Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.08464 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912889161383936 |
|---|---|
| author | Mughal, Ali Hassaan |
| author_facet | Mughal, Ali Hassaan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08464 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Autonomous RL Agent Methodology for Dynamic Web UI Testing in a BDD Framework Mughal, Ali Hassaan Software Engineering 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. |
| title | An Autonomous RL Agent Methodology for Dynamic Web UI Testing in a BDD Framework |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2503.08464 |