TEASMA: A Practical Approach for the Test Assessment of Deep Neural Networks using Mutation Analysis

TEASMA: A Practical Approach for the Test Assessment of Deep Neural Networks using Mutation Analysis. Abbasishahkoo, A., Dadkhah, M., Briand, L. C., & Lin, D. CoRR, 2023.

Paper doi bibtex

@article{DBLP:journals/corr/abs-2308-01311,
  author       = {Amin Abbasishahkoo and
                  Mahboubeh Dadkhah and
                  Lionel C. Briand and
                  Dayi Lin},
  title        = {{TEASMA:} {A} Practical Approach for the Test Assessment of Deep Neural
                  Networks using Mutation Analysis},
  journal      = {CoRR},
  volume       = {abs/2308.01311},
  year         = {2023},
  url          = {https://doi.org/10.48550/arXiv.2308.01311},
  doi          = {10.48550/ARXIV.2308.01311},
  eprinttype    = {arXiv},
  eprint       = {2308.01311},
  timestamp    = {Mon, 21 Aug 2023 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2308-01311.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

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