EAGLE: Creating Equivalent Graphs to Test Deep Learning Libraries

Jiannan Wang, Thibaud Lutellier, Shangshu Qian, Hung Viet Pham, Lin Tan

Abstract

Testing deep learning (DL) software is crucial and challenging. Recent approaches use differential testing to cross-check pairs of implementations of the same functionality across different libraries. Such approaches require two DL libraries implementing the same functionality, which is often unavailable. In addition, they rely on a high-level library, Keras, that implements missing functionality in all supported DL libraries, which is prohibitively expensive and thus no longer maintained.

BibTeX

@inproceedings{Wang-al:ICSE22,
  author    = {Jiannan Wang and
               Thibaud Lutellier and
               Shangshu Qian and
               Hung Viet Pham and
               Lin Tan},
  title     = {{EAGLE:} Creating Equivalent Graphs to Test Deep Learning Libraries},
  booktitle = {ICSE},
  pages     = {798--810},
  publisher = {{ACM}},
  year      = {2022},
}

EAGLE: Creating Equivalent Graphs to Test Deep Learning Libraries

Abstract

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