Automatic web testing using curiosity-driven reinforcement learning

Yan Zheng, Yi Liu, Xiaofei Xie, Yepang Liu, Lei Ma, Jianye Hao, Yang Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

38 Citations (Scopus)


Web testing has long been recognized as a notoriously difficult task. Even nowadays, web testing still mainly relies on manual efforts in many cases while automated web testing is still far from achieving human-level performance. Key challenges 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 space of all possible sequences. In this paper, we propose WebExplor, an automatic end-to-end web testing framework, to achieve an adaptive exploration of web applications. WebExplor adopts a curiosity-driven reinforcement learning to generate high-quality action sequences (test cases) with temporal logical relations. Besides, WebExplor incrementally builds an automaton during the online testing process, which acts as the high-level guidance to further improve the testing efficiency. We have conducted comprehensive evaluations 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.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE/ACM 43rd International Conference on Software Engineering, ICSE 2021
PublisherIEEE Computer Society
Number of pages13
ISBN (Electronic)9780738113197
Publication statusPublished - May 2021
Event43rd IEEE/ACM International Conference on Software Engineering, ICSE 2021 - Virtual, Online, Spain
Duration: May 22 2021May 30 2021

Publication series

NameProceedings - International Conference on Software Engineering
ISSN (Print)0270-5257


Conference43rd IEEE/ACM International Conference on Software Engineering, ICSE 2021
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • Software


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