Software Engineering Practices for Machine Learning

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2014

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2016

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2017

Cited alongside, same era.

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2017

Cited alongside, same era.

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2017

Cited alongside, same era.

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2018

Cited alongside, same era.

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2018

Cited alongside, same era.

E. Nascimento, I. Ahmed, E. Oliveira, M. Palheta, I. Steinmacher, and T. Conte, “Understanding development process of machine learning systems: Challenges and solutions,” in 2019 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) . Los Alamitos, CA, USA: IEEE Computer Society, sep 2019, pp. 1–6. [Online]. Available: https://doi.ieeecomputersociety.org/10.1109/ESEM.2019.8870157

2019

Closest in time.

L. E. Lwakatare, A. Raj, J. Bosch, H. H. Olsson, and I. Crnkovic, “A taxonomy of software engineering challenges for machine learning systems: An empirical investigation,” in Agile Processes in Software Engineering and Extreme Programming , P. Kruchten, S. Fraser, and F. Coallier, Eds. Cham: Springer International Publishing, 2019, pp. 227–243

2019

Closest in time.

T. Zhang, C. Gao, L. Ma, M. Lyu, and M. Kim, “An empirical study of common challenges in developing deep learning applications,” in 2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE) , 2019, pp. 104–115

2019

Closest in time.