2018

CODIT: Code Editing with Tree-Based Neural Models

Chakraborty, Saikat, Ding, Yangruibo, Allamanis, Miltiadis et al.

Understand

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 general introduces some specific problems that needs specific attention from research community.

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