Zhang, JM;
Harman, M;
Ma, L;
Liu, Y;
(2020)
Machine Learning Testing: Survey, Landscapes and Horizons.
IEEE Transactions on Software Engineering
10.1109/TSE.2019.2962027.
(In press).
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Abstract
This paper provides a comprehensive survey of Machine Learning Testing (ML testing) research. It covers 138 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 machine learning testing.
| Type: | Article |
|---|---|
| Title: | Machine Learning Testing: Survey, Landscapes and Horizons |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TSE.2019.2962027 |
| Publisher version: | https://doi.org/10.1109/TSE.2019.2962027 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | machine learning, software testing, deep neural network |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10099087 |
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