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Learning Semantic Program Embeddings with Graph Interval Neural Network by Wang, Yu; Gao, Fengjuan; Wang, Linzhang; Wang, Ke is a scholarly article available to read on EtoBox.

What is Learning Semantic Program Embeddings with Graph Interval Neural Network about?

Learning distributed representations of source code has been a challenging task for machine learning models. Earlier works treated programs as text so that natural language methods can be readily applied. Unfortunately, such approaches do not capitalize on the rich structural information possessed by source code. Of late, Graph Neural Network (GNN) was proposed to learn embeddings of programs from their graph representations. Due to the homogeneous and expensive message-passing procedure, GNN can suffer from precision issues, especially when dealing with programs rendered into large graphs. In this paper, we present a new graph neural architecture, called Graph Interval Neural Network (GINN), to tackle the weaknesses of the existing GNN. Unlike the standard GNN, GINN generalizes from a curated graph representation obtained through an abstraction method designed to aid models to learn. In particular, GINN focuses exclusively on intervals for mining the feature representation of a program, furthermore, GINN operates on a hierarchy of intervals for scaling the learning to large graphs. We evaluate GINN for two popular downstream applications: variable misuse prediction and method name

Author

Wang, Yu; Gao, Fengjuan; Wang, Linzhang; Wang, Ke

Published

2020

Language

EN