{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-reinforcement-learning-based-exploration","title":"Deep Reinforcement Learning-based Exploration of Web Applications","arxiv_id":null,"date":"2023-05-15","proceeding":"International Journal of Information & Communication Technology Research 2023 5","authors":["Mohammadreza Abbasnezhad","Amir Jahangard Rafsanjani","Amin Milani Fard"],"abstract":"Web application (app) exploration is a crucial part of various analysis and testing techniques. However, the \r\ncurrent methods are not able to properly explore the state space of web apps. As a result, techniques must be developed \r\nto guide the exploration in order to get acceptable functionality coverage for web apps. Reinforcement Learning (RL) \r\nis a machine learning method in which the best way to do a task is learned through trial and error, with the help of \r\npositive or negative rewards, instead of direct supervision. Deep RL is a recent expansion of RL that makes use of neural \r\nnetworks’ learning capabilities. This feature makes Deep RL suitable for exploring the complex state space of web apps. \r\nHowever, current methods provide fundamental RL. In this research, we offer DeepEx, a Deep RL-based exploration \r\nstrategy for systematically exploring web apps. Empirically evaluated on seven open-source web apps, DeepEx \r\ndemonstrated a 17% improvement in code coverage and a 16% enhancement in navigational diversity over the state\r\nof-the-art RL-based method. Additionally, it showed a 19% increase in structural diversity. These results confirm the \r\nsuperiority of Deep RL over traditional RL methods in web app exploration.","url_abs":"https://journal.itrc.ac.ir/article-1-604-en.pdf","url_pdf":"https://journal.itrc.ac.ir/article-1-604-en.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-reinforcement-learning-based-exploration","repo_url":"https://github.com/aktastunahan/DeepEx","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}