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

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

    ISSN: 1382-3256, 1573-7616
    Published: New York Springer US 01.06.2023
    “…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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    Journal Article
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    A controlled experiment of different code representations for learning-based program repair by Namavar, Marjane, Nashid, Noor, Mesbah, Ali

    ISSN: 1382-3256, 1573-7616
    Published: New York Springer US 01.12.2022
    “…Training a deep learning model on source code has gained significant traction recently…”
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    Journal Article
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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

    ISSN: 1382-3256, 1573-7616
    Published: New York Springer US 01.03.2023
    “…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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    Journal Article
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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

    ISSN: 1382-3256, 1573-7616
    Published: New York Springer US 01.12.2022
    “… 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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    Journal Article
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    Neural Network Guided Spatial Fault Resilience in Array Processors by Sindia, Suraj, Agrawal, Vishwani D.

    ISSN: 0923-8174, 1573-0727
    Published: Boston Springer US 01.08.2013
    Published in Journal of electronic testing (01.08.2013)
    “…-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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    Journal Article