Automatic Web Testing Using Curiosity-Driven Reinforcement Learning

Zheng, Yan1,2,4,5; Liu, Yi2,3,4; Xie, Xiaofei2; Liu, Yepang3; Ma, Lei4; Hao, Jianye1; Liu, Yang2

10.1109/ICSE43902.2021.00048

2021

43rd IEEE/ACM International Conference on Software Engineering - Software Engineering in Practice (ICSE-SEIP) / 43rd ACM/IEEE International Conference on Software Engineering - New Ideas and Emerging Results (ICSE-NIER)

0270-5257

978-1-6654-0296-5

2021 IEEE/ACM 43RD INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2021)

423-435

MAY 25-28, 2021

null,null,ELECTR NETWORK

10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA

IEEE COMPUTER SOC

Web testing has long been recognized as a notoriously difficult task. Even nowadays. web testing still heavily relies on manual efforts while automated web testing is far from achieving human-level performance. Key challenges in web testing include dynamic content update and deep bugs hiding under complicated user interactions and specific input values, which can only be triggered by certain action sequences in the huge search space. In this paper, we propose WebExplor, an automatic end-to-end web testing framework, to achieve an adaptive exploration of web applications. WebExplor adopts curiosity-driven reinforcement learning to generate high-quality action sequences (test cases) satisfying temporal logical relations. Besides. WebExplor incrementally builds an automaton during the online testing process, which provides high-level guidance to further improve the testing efficiency. We have conducted comprehensive evaluations of WebExplor on six real-world projects, a commercial SaaS web application, and performed an in-the-wild study of the top 50 web applications in the world. The results demonstrate that in most cases WebExplor can achieve significantly higher failure detection rate, code coverage and efficiency than existing state-of-the-art web testing techniques. WebExplor also detected 12 previously unknown failures in the commercial web application, which have been confirmed and fixed by the developers. Furthermore, our in-the-wild study further uncovered 3,466 exceptions and errors.

Web testing Reinforcement Learning Curiosity Exploration Software Engineering

其他

英语

CPCI-S ; EI

National Natural Science Foundation of China[U1836214]

Computer Science

Computer Science, Software Engineering ; Computer Science, Theory & Methods

WOS:000684601800035

20213910949815

Efficiency ; Reinforcement learning ; Websites

Artificial Intelligence:723.4 ; Computer Applications:723.5 ; Production Engineering:913.1

Web of Science

1.Tianjin Univ, Tianjin, Peoples R China
2.Nanyang Technol Univ, Singapore, Singapore
3.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Guangdong Prov Key Lab Brain Inspired Intelligent, Shenzhen, Peoples R China
4.Kyushu Univ, Fukuoka, Japan
5.Tianjin Univ, Sch New Media & Commun, Beijing, Peoples R China

Zheng, Yan,Liu, Yi,Xie, Xiaofei,et al. Automatic Web Testing Using Curiosity-Driven Reinforcement Learning[C]. 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA:IEEE COMPUTER SOC,2021:423-435.