TEASMA: A Practical Methodology for Test Adequacy Assessment of Deep Neural Networks

TEASMA: A Practical Methodology for Test Adequacy Assessment of Deep Neural Networks. Abbasishahkoo, A., Dadkhah, M., Briand, L. C., & Lin, D. IEEE Trans. Software Eng., 50(12):3307–3329, 2024.

Paper doi bibtex

@article{DBLP:journals/tse/AbbasishahkooDBL24,
  author       = {Amin Abbasishahkoo and
                  Mahboubeh Dadkhah and
                  Lionel C. Briand and
                  Dayi Lin},
  title        = {{TEASMA:} {A} Practical Methodology for Test Adequacy Assessment of
                  Deep Neural Networks},
  journal      = {{IEEE} Trans. Software Eng.},
  volume       = {50},
  number       = {12},
  pages        = {3307--3329},
  year         = {2024},
  url          = {https://doi.org/10.1109/TSE.2024.3482984},
  doi          = {10.1109/TSE.2024.3482984},
  timestamp    = {Wed, 08 Jan 2025 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/tse/AbbasishahkooDBL24.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

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