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Machine Learning Testing: Survey, Landscapes and Horizons
[article]
2019
arXiv
pre-print
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
arXiv:1906.10742v2
fatcat:p5c54cy4pjc5flzm7shybk3qxe