Method for learning function hierarchy embedding representation in source codes in hyperbolic space

Abstract

The invention discloses a method for learning function hierarchy embedding representation in a source code in a hyperbolic space. The method comprises the following steps: S1, data collection and construction stage, S2, a Richi curvature analysis stage; S3, a Poincare model learning function embedding representation stage., The latest data mining technology, dimension reduction technology, deep learning processing technology and visualization technology are applied to hyperbolic space function embedded learning, and mass data, such as source code data can be effectively contained; data dimension reduction is efficiently and accurately achieved, and then hierarchical information is visually analyzed..

Description

technical field [0001] The invention relates to the technical field of network research, in particular to a function-level embedding representation method for learning source codes in a hyperbolic space. Background technique [0002] The successful application of distributed representations in natural language processing and the naturalness assumption have inspired researchers to apply distributed representations to source code mining. Most existing work on learning distributed representations from source code typically treats programs as sequences of symbols or bags of words. Some of them first parse the program to obtain the corresponding Abstract Syntax Tree (AST), which is suitable for tree-based neural networks or expand the AST to a graph for further processing. According to these underlying neural network models, existing works can generally be classified into the following categories: feed-forward neural networks, recurrent neural networks, convolutional neural netw...

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Problems solved by technology

[0005] Aiming at the deficiencies of existing research methods, the present invention provides a method for learning the hierarchical embedding representation of functions in source codes in hyperbolic space, which solves the problems of lack of hierarchical information and insufficient space in existing research methods

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