Lessons learned from hyper-parameter tuning for microservice candidate identification
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Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9678507/9678392/09678704.pdf?arnumber=9678704
Reference19 articles.
1. Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models
2. Service candidate identification from monolithic systems based on execution traces;jin;IEEE Transactions on Software Engineering,2019
3. Mono2Micro: an AI-based toolchain for evolving monolithic enterprise applications to a microservice architecture
4. Semi-supervised classification with graph convolutional networks;kipf;arXiv preprint arXiv 1609 02907,2016
5. Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-Learn
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