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Publication Detail
Machine Learning Testing: Survey, Landscapes and Horizons
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Article
  • Authors:
    Zhang JM, Harman M, Ma L, Liu Y
  • Publisher:
    Institute of Electrical and Electronics Engineers
  • Publication date:
    17/02/2020
  • Journal:
    IEEE Transactions on Software Engineering
  • Status:
    Published
  • Print ISSN:
    0098-5589
  • Keywords:
    cs.LG, cs.LG, cs.AI, cs.SE, stat.ML
Abstract
This paper provides a comprehensive survey of Machine Learning Testing (ML testing) research. It covers 144 papers on testing properties (e.g., correctness, robustness, and fairness), testing components (e.g., the data, learning program, and framework), testing workflow (e.g., test generation and test evaluation), and application scenarios (e.g., autonomous driving, machine translation). The paper also analyses trends concerning datasets, research trends, and research focus, concluding with research challenges and promising research directions in ML testing.
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