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Deep Reinforcement Learning-based Exploration of Web Applications
Mohammadreza Abbasnezhad1 , Amir Jahangard Rafsanjani 2, Amin Milani Fard3
1- Dept. of Computer Engineering, Yazd University, Yazd, Iran
2- Dept. of Computer Engineering, Yazd University, Yazd, Iran , jahangard@yazd.ac.ir
3- Dept. of Computer Science, New York Inst. of Technology, Vancouver, BC, Canada.
Abstract:   (256 Views)
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.
Keywords: Deep Reinforcement Learning, Exploration, Model Generation, Web Application
Full-Text [DOCX 237 kb]   (28 Downloads)    
Type of Study: Research | Subject: Information Technology
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.