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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Veröffentlicht in:IEEE transactions on software engineering Jg. 48; H. 1; S. 1 - 36
Hauptverfasser: Zhang, Jie M., Harman, Mark, Ma, Lei, Liu, Yang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.01.2022
IEEE Computer Society
Schlagworte:
Data models
deep neural network
Learning programs
Machine learning
Machine translation
Robustness
Software engineering
Software testing
Training data
Trends
Workflow
ISSN:0098-5589, 1939-3520
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Zusammenfassung: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), 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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0098-5589
1939-3520
DOI:10.1109/TSE.2019.2962027