Výsledky vyhľadávania - Special Issue on Software Testing in the Machine Learning Era

Software testing in the machine learning era: Special issue of the empirical Software Engineering (EMSE) journal Autor Stocco, Andrea, Shehory, Onn, Jahangirova, Gunel, Riccio, Vincenzo, Barash, Guy, Farchi, Eitan, Saha, Diptikalyan

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Software testing in the machine learning era Autor Stocco, Andrea, Shehory, Onn, Jahangirova, Gunel, Riccio, Vincenzo, Barash, Guy, Farchi, Eitan, Saha, Diptikalyan

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Bugs in machine learning-based systems: a faultload benchmark Autor 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…”
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A controlled experiment of different code representations for learning-based program repair Autor Namavar, Marjane, Nashid, Noor, Mesbah, Ali

“…Training a deep learning model on source code has gained significant traction recently…”
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Towards understanding quality challenges of the federated learning for neural networks: a first look from the lens of robustness Autor 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…”
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DiverGet: a Search-Based Software Testing approach for Deep Neural Network Quantization assessment Autor Yahmed, Ahmed Haj, Braiek, Houssem Ben, Khomh, Foutse, Bouzidi, Sonia, Zaatour, Rania

“… Traditional testing methods, which gather more and more genuine data for better assessment, are often not practical because of the large size of the input…”
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Neural Network Guided Spatial Fault Resilience in Array Processors Autor Sindia, Suraj, Agrawal, Vishwani D.

“…-to-chip and within-chip variation of MOSFET threshold voltage. In this paper, we propose a software-framework using machine learning for in-situ prediction and correction of computation corrupted due to threshold voltage variation of transistors…”
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