Machine Learning Testing: Survey, Landscapes and Horizons

This paper provides a comprehensive survey of techniques for testing machine learning systems; 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...

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Bibliographic Details
Published in:IEEE transactions on software engineering 2022-01, Vol.48 (1), p.1-36
Main Authors: Zhang, Jie M., Harman, Mark, Ma, Lei, Liu, Yang
Format: Article
Language:English
Subjects:
Data models
deep neural network
Learning programs
Machine learning
Machine translation
Robustness
Software engineering
Software testing
Training data
Trends
Workflow
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