Learning the Relation between Code Features and Code Transforms with Structured Prediction

22 Jul 2019Zhongxing YuMatias MartinezTegawendé F. BissyandéMartin Monperrus

We present in this paper the first approach for structurally predicting code transforms at the level of AST nodes using conditional random fields. Our approach first learns offline a probabilistic model that captures how certain code transforms are applied to certain AST nodes, and then uses the learned model to predict transforms for new, unseen code snippets... (read more)

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