Structcoder: Structure-aware transformer for code generation
There has been a recent surge of interest in automating software engineering tasks using
deep learning. This article addresses the problem of code generation, in which the goal is to …
deep learning. This article addresses the problem of code generation, in which the goal is to …
Treegen: A tree-based transformer architecture for code generation
A code generation system generates programming language code based on an input
natural language description. State-of-the-art approaches rely on neural networks for code …
natural language description. State-of-the-art approaches rely on neural networks for code …
What do pre-trained code models know about code?
A Karmakar, R Robbes - 2021 36th IEEE/ACM International …, 2021 - ieeexplore.ieee.org
Pre-trained models of code built on the transformer architecture have performed well on
software engineering (SE) tasks such as predictive code generation, code summarization …
software engineering (SE) tasks such as predictive code generation, code summarization …
A grammar-based structural cnn decoder for code generation
Code generation maps a program description to executable source code in a programming
language. Existing approaches mainly rely on a recurrent neural network (RNN) as the …
language. Existing approaches mainly rely on a recurrent neural network (RNN) as the …
Embedding API dependency graph for neural code generation
The problem of code generation from textual program descriptions has long been viewed as
a grand challenge in software engineering. In recent years, many deep learning based …
a grand challenge in software engineering. In recent years, many deep learning based …
Deep learning meets software engineering: A survey on pre-trained models of source code
Recent years have seen the successful application of deep learning to software engineering
(SE). In particular, the development and use of pre-trained models of source code has …
(SE). In particular, the development and use of pre-trained models of source code has …
A systematic mapping study of source code representation for deep learning in software engineering
The usage of deep learning (DL) approaches for software engineering has attracted much
attention, particularly in source code modelling and analysis. However, in order to use DL …
attention, particularly in source code modelling and analysis. However, in order to use DL …
Probing pretrained models of source code
S Troshin, N Chirkova - arXiv preprint arXiv:2202.08975, 2022 - arxiv.org
Deep learning models are widely used for solving challenging code processing tasks, such
as code generation or code summarization. Traditionally, a specific model architecture was …
as code generation or code summarization. Traditionally, a specific model architecture was …
Maybe deep neural networks are the best choice for modeling source code
RM Karampatsis, C Sutton - arXiv preprint arXiv:1903.05734, 2019 - arxiv.org
Statistical language modeling techniques have successfully been applied to source code,
yielding a variety of new software development tools, such as tools for code suggestion and …
yielding a variety of new software development tools, such as tools for code suggestion and …
Two birds with one stone: Boosting code generation and code search via a generative adversarial network
Automatically transforming developers' natural language descriptions into source code has
been a longstanding goal in software engineering research. Two types of approaches have …
been a longstanding goal in software engineering research. Two types of approaches have …