一句话：提出 WebExplor，用好奇心驱动强化学习生成动作序列并增量构建自动机引导，显著提升 Web 测试的故障检测率与覆盖率。
Web 测试长期依赖人工，自动化水平远未达到人类水平，核心挑战在于动态内容更新以及深层缺陷只能由巨大动作序列空间中特定序列配合特定输入触发。本文提出端到端自动化 Web 测试框架 WebExplor，采用好奇心驱动的强化学习生成具有时序逻辑关系的高质量动作序列（测试用例），并在在线测试过程中增量构建自动机，作为高层引导进一步提升测试效率。作者在六个真实项目、一个商业 SaaS 应用以及全球前 50 的 Web 应用上开展了全面评估与野外研究。结果表明，在大多数情况下 WebExplor 的故障检测率、代码覆盖率和效率均显著优于现有最先进技术；在商业应用中检测到 12 个此前未知的故障，已获开发者确认并修复；野外研究进一步发现 3,466 个异常与错误。
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.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{ZhengLXLMHL21,
title = {Automatic Web Testing Using Curiosity-Driven Reinforcement Learning},
author = {Yan Zheng and Yi Liu and Xiaofei Xie and Yepang Liu and Lei Ma and Jianye Hao and Yang Liu},
booktitle = {ICSE},
year = {2021},
doi = {10.1109/icse43902.2021.00048}
}