deep reinforcement learning-based exploration of web applications

Web application (app) exploration is a crucial part of various analysis and testing techniques. however, the current methods are not able to properly explore the state space of web apps. as a result, techniques must be developed to guide the exploration in order to get acceptable functionality coverage for web apps. reinforcement learning (rl) is a machine learning method in which the best way to do a task is learned through trial and error, with the help of positive or negative rewards, instead of direct supervision. deep rl is a recent expansion of rl that makes use of neural networks’ learning capabilities. this feature makes deep rl suitable for exploring the complex state space of web apps. however, current methods provide fundamental rl. in this research, we offer deepex, a deep rl-based exploration strategy for systematically exploring web apps. empirically evaluated on seven open-source web apps, deepex demonstrated a 17% improvement in code coverage and a 16% enhancement in navigational diversity over the stateof-the-art rl-based method. additionally, it showed a 19% increase in structural diversity. these results confirm the superiority of deep rl over traditional rl methods in web app exploration.