Java Code Generation consists in generating automatically Java code from a
Natural Language Text. This NLP task helps in increasing programmers'
productivity by providing them with immediate solutions to the simplest and
most repetitive tasks. Code generation is a challenging task because of the
hard syntactic rules and the necessity of a deep understanding of the semantic
aspect of the programming language. Many works tried to tackle this task using
either RNN-based, or Transformer-based models. The latter achieved remarkable
advancement in the domain and they can be divided into three groups: (1)
encoder-only models, (2) decoder-only models, and (3) encoder-decoder models.
In this paper, we provide a comprehensive review of the evolution and progress
of deep learning models in Java code generation task. We focus on the most
important methods and present their merits and limitations, as well as the
objective functions used by the community. In addition, we provide a detailed
description of datasets and evaluation metrics used in the literature. Finally,
we discuss results of different models on CONCODE dataset, then propose some
future directions.

本文主要综述了深度学习模型在 Java 代码生成任务中的进展和发展，涉及到自然语言处理、基于 RNN 和 Transformer 的模型、CONCODE 数据集等方面，并对存在的方法、数据集、评估指标、未来发展等方面进行了详细介绍。

从自然语言文本生成 Java 代码的现有技术方法综述

A Comprehensive Review of State-of-The-Art Methods for Java Code  Generation from Natural Language Text

Pretrained transformer-based models have shown high performance in natural
language generation task. However, a new wave of interest has surged: automatic
programming language generation. This task consists of translating natural
language instructions to a programming code. Despite the fact that well-known
pretrained models on language generation have achieved good performance in
learning programming languages, effort is still needed in automatic code
generation. In this paper, we introduce JaCoText, a model based on Transformers
neural network. It aims to generate java source code from natural language
text. JaCoText leverages advantages of both natural language and code
generation models. More specifically, we study some findings from the state of
the art and use them to (1) initialize our model from powerful pretrained
models, (2) explore additional pretraining on our java dataset, (3) carry out
experiments combining the unimodal and bimodal data in the training, and (4)
scale the input and output length during the fine-tuning of the model.
Conducted experiments on CONCODE dataset show that JaCoText achieves new
state-of-the-art results.

本文介绍了一种基于 Transformer 神经网络的模型 JaCoText，旨在将自然语言文本生成 java 源代码。通过在强大的预训练模型上初始化，探索我们的 java 数据集上的额外预训练，以及在训练中结合单模态和双模态数据的实验，以及在模型的微调期间缩放输入和输出长度等方法，经过 CONCODE 数据集的实验表明，JaCoText 取得了新的最先进的结果。