Adaptive Test Selection for Deep Neural Networks

Xinyu Gao, Yang Feng, Yining Yin, Zixi Liu, Zhenyu Chen, Baowen Xu

Abstract

Deep neural networks (DNN) have achieved tremendous development in the past decade. While many DNN-driven software applications have been deployed to solve various tasks, they could also produce incorrect behaviors and result in massive losses. To reveal the incorrect behaviors and improve the quality of DNN-driven applications, developers often need rich labeled data for the testing and optimization of DNN models. However, in practice, collecting diverse data from application scenarios and labeling them properly is often a highly expensive and time-consuming task.

BibTeX

@inproceedings{Gao-al:ICSE22,
  author    = {Xinyu Gao and
               Yang Feng and
               Yining Yin and
               Zixi Liu and
               Zhenyu Chen and
               Baowen Xu},
  title     = {Adaptive Test Selection for Deep Neural Networks},
  booktitle = {ICSE},
  pages     = {73--85},
  publisher = {{ACM}},
  year      = {2022},
}

Adaptive Test Selection for Deep Neural Networks

Abstract

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