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.
@article{arxiv.1906.10742,
title = {Machine Learning Testing: Survey, Landscapes and Horizons},
author = {Jie M. Zhang and Mark Harman and Lei Ma and Yang Liu},
journal= {arXiv preprint arXiv:1906.10742},
year = {2019}
}