Machine Learning Testing: Survey, Landscapes and Horizons
It synthesizes existing work for researchers and practitioners in ML testing, but is incremental as a survey.
This paper provides a comprehensive survey of Machine Learning Testing (ML testing) research, covering 144 papers on testing properties, components, workflow, and application scenarios, and concludes with research challenges and directions.
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.