A Reinforcement Learning Approach to Generate Test Cases for Web Applications
Web applications play an important role in modern society. Quality assurance of web applications requires lots of manual efforts. In this demo paper, we propose emph{WebQT}, an automatic test case generator for web applications based on reinforcement learning. Specifically, to increase testing efficiency, we design a new reward model, which encourages the agent to mimic human testers to interact with the web applications. To alleviate the state space explosion problem during the exploration, we further propose a novel state abstraction technique, which can identify different web pages with the same functionality as the same state, and yields a simplified state space. We evaluate WebQT on seven open-source web applications. The experimental results show that WebQT achieves 45.4% more code coverage along with higher efficiency than the state-of-the-art technique. In addition, WebQT also reveals 69 failures in 11 real-world web applications.
Mon 15 MayDisplayed time zone: Hobart change
11:00 - 12:30 | |||
11:00 22mTalk | An Method of Intelligent Duplicate Bug Report Detection Based on Technical Term Extraction AST 2023 Xiaoxue Wu Yangzhou University, Wenjing Shan Yangzhou University, Wei Zheng Northwestern Polytechnical University, Zhiguo Chen Northwestern Polytechnical University, Tao Ren Yangzhou University, Xiaobing Sun Yangzhou University | ||
11:22 22mTalk | A Reinforcement Learning Approach to Generate Test Cases for Web Applications AST 2023 Xiaoning Chang Institute of Software, Chinese Academy of Sciences, Zheheng Liang Joint Laboratory on Cyberspace Security of China Southern Power Grid, Yifei Zhang State Key Lab of Computer Sciences, Institute of Software, Chinese Academy of Sciences, Lei Cui Joint Laboratory on Cyberspace Security of China Southern Power Grid, Zhenyue Long , Guoquan Wu Institute of Software at Chinese Academy of Sciences; University of Chinese Academy of Sciences; University of Chinese Academy of Sciences Nanjing College; China Southern Power Grid, Yu Gao Institute of Software, Chinese Academy of Sciences, China, Wei Chen Institute of Software at Chinese Academy of Sciences; University of Chinese Academy of Sciences; University of Chinese Academy of Sciences Nanjing College, Jun Wei Institute of Software at Chinese Academy of Sciences; University of Chinese Academy of Sciences; University of Chinese Academy of Sciences Chongqing School, Tao Huang Institute of Software Chinese Academy of Sciences | ||
11:45 22mTalk | Cross-Project setting using Deep learning Architectures in Just-In-Time Software Fault Prediction: An Investigation AST 2023 Sushant Kumar Pandey Chalmers and University of Gothenburg, Anil Kumar Tripathi Indian Institute of Technology (BHU), Varanasi | ||
12:07 22mTalk | On Comparing Mutation Testing Tools through Learning-based Mutant Selection AST 2023 Milos Ojdanic University of Luxembourg, Ahmed Khanfir University of Luxembourg, Aayush Garg University of Luxembourg, Luxembourg, Renzo Degiovanni SnT, University of Luxembourg, Mike Papadakis University of Luxembourg, Luxembourg, Yves Le Traon University of Luxembourg, Luxembourg File Attached | ||