Integrating Non-Fourier and AST-Structural Relative Position Representations Into Transformer-Based Model for Source Code Summarization

Integrating Non-Fourier and AST-Structural Relative Position Representations Into Transformer-Based Model for Source Code Summarization

Source code summaries play a crucial role in helping programmers comprehend the behavior of source code functions. In recent deep-learning based approaches for Source Code Summarization, there has been a growing focus on Transformer-based models. These models use self-attention mechanisms to overcom...

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Bibliographic Details
Main Authors: Hsiang-Mei Liang, Chin-Yu Huang
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Abstract syntax tree
deep learning
machine translation
natural language
program comprehension
positional encoding
Online Access:https://ieeexplore.ieee.org/document/10400421/

Internet

https://ieeexplore.ieee.org/document/10400421/

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