Using Webpage Comparison Method for Automated Web Application Testing with Reinforcement Learning - Ci-Feng Lai; Chien-Hung Liu; Shingchern D. You (International Journal of Engineering and Technology Innovation, 2025)
Web application testing often uses crawlers to explore the application under test (AUT) and identify potential vulnerabilities. For dynamically generated pages, crawlers must provide test inputs for web forms. A previous tool combines a web crawler with a reinforcement learning agent, which uses code coverage to guide the crawler in filling web forms. This paper aims to improve the applicability of web application testing by using webpage comparison techniques instead of code coverage and source code access, thereby enhancing the handling of multiple web forms on a single page. Experimental results show that this approach explores more pages, reaches greater crawling depths, and achieves better code coverage than the original method. It also interacts more efficiently with multiple web forms and outperforms a random-action Monkey on new, untrained web applications. Therefore, this approach is promising for automated web application testing.