Java ↔ C#, Java ↔ Python, Java ↔ C++, Python ↔ C++

本研究利用文档相似性方法创建了用于代码翻译的平行数据集，主要涉及Java、C#、Python和C++等编程语言。数据集通过分析开源项目中的代码实现，匹配具有相同功能但语言不同的代码片段。这些数据集用于训练模型，以实现不同编程语言间的自动代码翻译。研究展示了即使在数据集中存在一定程度的噪声，训练出的模型仍能保持良好的翻译性能。此外，研究还扩展了可自动翻译的编程语言对，进一步证明了该方法的实用性和有效性。

This study utilizes a document similarity methodology to develop parallel datasets for code translation, which primarily cover programming languages including Java, C#, Python, C++ and other major programming languages. The datasets are constructed by analyzing code implementations from open-source projects and pairing code snippets that share identical functionality but are written in distinct programming languages. These datasets serve as training data for models designed to achieve automatic code translation between different programming languages. The study demonstrates that the trained models can maintain robust translation performance even when a certain level of noise exists in the datasets. Furthermore, the research has expanded the range of automatically translatable programming language pairs, further validating the practicality and effectiveness of the proposed approach.

搜集汇总

背景与挑战

背景概述

该数据集是一个用于代码翻译的平行数据集，涵盖Java、C#、Python和C++等编程语言之间的转换。它通过文档相似性方法从开源项目中提取功能相同的代码片段构建而成，用于训练自动翻译模型，即使在噪声存在下也能保持良好性能，并扩展了可翻译语言对，体现了其实用性和有效性。