A Method and Experiment to evaluate Deep Neural Networks as Test Oracles for Scientific Software
Valdivino Alexandre de Santiago Júnior received the PhD degree in Applied Computing from the Instituto Nacional de Pesquisas Espaciais (INPE), Brazil, in 2011, the MSc and BSc degrees in Electrical Engineering from the Universidade Federal do Ceará (UFC), Brazil, in 1999 and 1996, respectively. In 2019, he was a visiting scholar, developing post-doc research, at the Computational Optimisation and Learning (COL) Lab, School of Computer Science, University of Nottingham, England (United Kingdom). He also developed research in formal verification of probabilistic systems at the Concordia University, Montreal, Canada, in 2015. He has over 25 years of professional experience working on research and development of aerospace software and systems. He has been receiving several awards at international and national conferences in the fields of computer science and engineering. Research interests include machine and deep learning, optimisation via hyper-heuristics and metaheuristics, and software testing.
Tue 17 MayDisplayed time zone: Eastern Time (US & Canada) change
12:05 - 13:25 | |||
12:05 30mLong-paper | Microservices Integrated Performance and Reliability Testing AST 2022 Matteo Camilli Free University of Bozen-Bolzano, Antonio Guerriero Università di Napoli Federico II, Andrea Janes Free University of Bozen-Bolzano, Barbara Russo , Stefano Russo Università di Napoli Federico II | ||
12:35 30mLong-paper | A Method and Experiment to evaluate Deep Neural Networks as Test Oracles for Scientific Software AST 2022 Valdivino Santiago Júnior INPE - National Institute for Space Research | ||
13:05 20mShort-paper | Model-Based Test Adaptation for Smart TVs AST 2022 Atıl Fırat , Mohammad Yusaf Azimi , Celal Çağın Elgün , Ferhat Erata Yale University, Cemal Yilmaz Sabancı University |