[3K4-IS-2a] Machine learning

[3K4-IS-2a-03] Automated Web Application Testing: End-to-End Test Case Generation with Large Language Models and Screen Transition Graphs

〇Khang Nguyen Le¹, Minh Ngoc Nguyen¹, Hiep Nguyen¹, Trung Vo¹, Son Luu¹, Quan Minh Bui², Shoshin Nomura², Minh Le Nguyen¹ (1. Japan Advanced Institute of Science and Technology, 2. Amifiable Inc.)

キーワード：Automated Web Testing

Web applications are critical to modern software ecosystems, yet ensuring their reliability remains challenging due to the complexity and dynamic nature of web interfaces. Recent advances in large language models (LLMs) have shown promise in automating complex tasks, but limitations persist in handling dynamic navigation flows and complex form interactions. This paper presents an automated system for generating test cases for two key aspects of web application testing: site navigation and form filling. For site navigation, the system employs screen transition graphs and LLMs to model navigation flows and generate test scenarios. For form filling, it uses state graphs to handle conditional forms and automates Selenium script generation. Key contributions include: (1) a novel integration of graph structures and LLMs for site navigation testing, (2) a state graph-based approach for automating form-filling test cases, and (3) a comprehensive dataset for evaluating form-interaction testing. Experimental results demonstrate the system's effectiveness in improving test coverage and robustness, advancing the state of web application testing.