Using LLM-Based Deep Reinforcement Learning Agents to Detect Bugs in Web Applications

Yuki Sakai, Yasuyuki Tahara, Akihiko Ohsuga, Yuichi Sei

2025

Abstract

This paper presents an approach to automate black-box GUI testing for web applications by integrating deep reinforcement learning (DRL) with large language models (LLMs). Traditional GUI testing is often inefficient and costly due to the difficulty in generating comprehensive test scenarios. While DRL has shown potential in automating exploratory testing by leveraging GUI interaction data, such data is browser-dependent and not always accessible in web applications. To address this challenge, we propose using LLMs to infer interaction information directly from HTML code, incorporating these inferences into the DRL’s state representation. We hypothesize that combining the inferential capabilities of LLMs with the robustness of DRL can match the accuracy of methods relying on direct data collection. Through experiments, we demonstrate that LLM-inferred interaction information effectively substitutes for direct data, enhancing both the efficiency and accuracy of automated GUI testing. Our results indicate that this approach not only streamlines GUI testing for web applications but also has broader implications for domains where direct state information is hard to obtain. The study suggests that integrating LLMs with DRL offers a promising path toward more efficient and scalable automation in GUI testing.

Paper Citation

in Harvard Style

Sakai Y., Tahara Y., Ohsuga A. and Sei Y. (2025). Using LLM-Based Deep Reinforcement Learning Agents to Detect Bugs in Web Applications. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 1001-1008. DOI: 10.5220/0013248800003890

in Bibtex Style

@conference{icaart25,
author={Yuki Sakai and Yasuyuki Tahara and Akihiko Ohsuga and Yuichi Sei},
title={Using LLM-Based Deep Reinforcement Learning Agents to Detect Bugs in Web Applications},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={1001-1008},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013248800003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Using LLM-Based Deep Reinforcement Learning Agents to Detect Bugs in Web Applications
SN - 978-989-758-737-5
AU - Sakai Y.
AU - Tahara Y.
AU - Ohsuga A.
AU - Sei Y.
PY - 2025
SP - 1001
EP - 1008
DO - 10.5220/0013248800003890
PB - SciTePress