ASE 2022★ Distinguished Paper

QATest: A Uniform Fuzzing Framework for Question Answering Systems

Zixi Liu, Yang Feng, Yining Yin, Jingyu Sun, Zhenyu Chen, Baowen Xu

Abstract

The tremendous advancements in deep learning techniques have empowered question answering(QA) systems with the capability of dealing with various tasks. Many commercial QA systems, such as Siri, Google Home, and Alexa, have been deployed to assist people in different daily activities. However, modern QA systems are often designed to deal with different topics and task formats, which makes both the test collection and labeling tasks difficult and thus threats their quality.

BibTeX

@inproceedings{Liu-al:ASE22,
  author    = {Zixi Liu and
               Yang Feng and
               Yining Yin and
               Jingyu Sun and
               Zhenyu Chen and
               Baowen Xu},
  title     = {{QATest:} A Uniform Fuzzing Framework for Question Answering Systems},
  booktitle = {ASE},
  pages     = {81:1--81:12},
  publisher = {{ACM}},
  year      = {2022},
}

QATest: A Uniform Fuzzing Framework for Question Answering Systems

Abstract

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