Recommended Reading

1. Eric Breck, Shanqing Cai, Eric Nielsen, Michael Salib, D. Sculley (2016). What’s your ML test score? A rubric for ML production systems. Reliable Machine Learning in the Wild - NIPS 2016 Workshop (2016) Preprint.
2. Zhang, J. M., Harman, M., Ma, L., & Liu, Y. (2020). Machine learning testing: Survey, landscapes and horizons. IEEE Transactions on Software Engineering. Preprint.
3. Sun, Z., Zhang, J. M., Harman, M., Papadakis, M., & Zhang, L. (2020, June). Automatic testing and improvement of machine translation. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering (pp. 974-985). Preprint.
4. Haakman, M., Cruz, L., Huijgens, H., & van Deursen, A. (2020). AI Lifecycle Models Need To Be Revised. An Exploratory Study in Fintech. Preprint.
5. van Oort, B., Cruz, L., Aniche, M., & van Deursen, A. (2021). The Prevalence of Code Smells in Machine Learning projects. Preprint.
6. Serban, A., van der Blom, K., Hoos, H., & Visser, J. (2020, October). Adoption and effects of software engineering best practices in machine learning. In Proceedings of the 14th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM). Preprint.
7. Christian Kästner. Machine Learning in Production: From Models to Products. Book chapters