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CODIT: Code Editing with Tree-Based Neural Models
[article]
2020
arXiv
pre-print
The way developers edit day-to-day code tends to be repetitive, often using existing code elements. Many researchers have tried to automate repetitive code changes by learning from specific change templates which are applied to limited scope. The advancement of deep neural networks and the availability of vast open-source evolutionary data opens up the possibility of automatically learning those templates from the wild. However, deep neural network based modeling for code changes and code in
arXiv:1810.00314v3
fatcat:jt4ihvprijevnm5iwnoo34f5pa