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• W3157295610 abstract "Machine learning contrasts with traditional software development in that the oracle is the data, and the data is not always a correct representation of the problem that machine learning tries to model. We present a survey of the oracle issues found in machine learning and state-of-the-art solutions for dealing with these issues. These include lines of research for differential testing, metamorphic testing, and test coverage. We also review some recent improvements to robustness during modeling that reduce the impact of oracle issues, as well as tools and frameworks for assisting in testing and discovering issues specific to the dataset." @default.
• W3157295610 created "2021-05-10" @default.
• W3157295610 creator A5020998174 @default.
• W3157295610 date "2021-05-04" @default.
• W3157295610 modified "2023-09-27" @default.
• W3157295610 title "A Review on Oracle Issues in Machine Learning." @default.
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• W3157295610 hasPublicationYear "2021" @default.
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