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}
}