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International Journal of Information and Communication Technology Research (IJICT، جلد ۱۶، شماره ۲، صفحات ۰-۰
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عنوان انگلیسی Deep Reinforcement Learning-based Exploration of Web Applications
چکیده انگلیسی مقاله 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. Our methodology was empirically evaluated on seven open-source web apps. DeepEx obtained greater code coverage with greater navigational and structural diversity than the existing state-of-the-art methods.
کلیدواژه‌های انگلیسی مقاله Deep Reinforcement Learning, Exploration, Model Generation, Web Application
نویسندگان مقاله | Mohammadreza Abbasnezhad
Dept. of Computer Engineering, Yazd University, Yazd, Iran


| Amir Jahangard Rafsanjani
Dept. of Computer Engineering, Yazd University, Yazd, Iran


| Amin Milani Fard
Dept. of Computer Science, New York Inst. of Technology, Vancouver, BC, Canada.
نشانی اینترنتی http://ijict.itrc.ac.ir/browse.php?a_code=A-10-4383-1&slc_lang=en&sid=1
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زبان مقاله منتشر شده en
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