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End to End Software Engineering Research
[article]
2021
arXiv
pre-print
End to end learning is machine learning starting in raw data and predicting a desired concept, with all steps done automatically. In software engineering context, we see it as starting from the source code and predicting process metrics. This framework can be used for predicting defects, code quality, productivity and more. End-to-end improves over features based machine learning by not requiring domain experts and being able to extract new knowledge. We describe a dataset of 5M files from 15k
arXiv:2112.11858v1
fatcat:ae6eptd4gjgmjpsq7ca5lqgexy