Search Results - "Special Issue on Software Testing in the Machine Learning Era"

Bugs in machine learning-based systems: a faultload benchmark by Morovati, Mohammad Mehdi, Nikanjam, Amin, Khomh, Foutse, Jiang, Zhen Ming (Jack)

“…The rapid escalation of applying Machine Learning (ML) in various domains has led to paying more attention to the quality of ML components. There is then a…”
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A controlled experiment of different code representations for learning-based program repair by Namavar, Marjane, Nashid, Noor, Mesbah, Ali

“…Training a deep learning model on source code has gained significant traction recently. Since such models reason about vectors of numbers, source code needs to…”
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Software testing in the machine learning era: Special issue of the empirical Software Engineering (EMSE) journal by Stocco, Andrea, Shehory, Onn, Jahangirova, Gunel, Riccio, Vincenzo, Barash, Guy, Farchi, Eitan, Saha, Diptikalyan

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Towards understanding quality challenges of the federated learning for neural networks: a first look from the lens of robustness by Abyane, Amin Eslami, Zhu, Derui, Souza, Roberto, Ma, Lei, Hemmati, Hadi

“…Federated learning (FL) is a distributed learning paradigm that preserves users’ data privacy while leveraging the entire dataset of all participants. In FL,…”
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DiverGet: a Search-Based Software Testing approach for Deep Neural Network Quantization assessment by Yahmed, Ahmed Haj, Braiek, Houssem Ben, Khomh, Foutse, Bouzidi, Sonia, Zaatour, Rania

“…Quantization is one of the most applied Deep Neural Network (DNN) compression strategies, when deploying a trained DNN model on an embedded system or a cell…”
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