Výsledky vyhledávání - Special Issue on Software Testing in the Machine Learning Era
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Software testing in the machine learning era: Special issue of the empirical Software Engineering (EMSE) journal
ISSN: 1382-3256, 1573-7616Vydáno: New York Springer US 01.05.2023Vydáno v Empirical software engineering : an international journal (01.05.2023)Získat plný text
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Software testing in the machine learning era
ISSN: 1382-3256, 1573-7616Vydáno: Dordrecht Springer Nature B.V 01.06.2023Vydáno v Empirical software engineering : an international journal (01.06.2023)Získat plný text
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Bugs in machine learning-based systems: a faultload benchmark
ISSN: 1382-3256, 1573-7616Vydáno: New York Springer US 01.06.2023Vydáno v Empirical software engineering : an international journal (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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A controlled experiment of different code representations for learning-based program repair
ISSN: 1382-3256, 1573-7616Vydáno: New York Springer US 01.12.2022Vydáno v Empirical software engineering : an international journal (01.12.2022)“…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
ISSN: 1382-3256, 1573-7616Vydáno: New York Springer US 01.03.2023Vydáno v Empirical software engineering : an international journal (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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DiverGet: a Search-Based Software Testing approach for Deep Neural Network Quantization assessment
ISSN: 1382-3256, 1573-7616Vydáno: New York Springer US 01.12.2022Vydáno v Empirical software engineering : an international journal (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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Neural Network Guided Spatial Fault Resilience in Array Processors
ISSN: 0923-8174, 1573-0727Vydáno: Boston Springer US 01.08.2013Vydáno v 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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