自然语言生成和对人工智能辅助编程大代码的理解:一篇评论
Man-Fai Wong1, Shangxin Guo2, Ching-Nam Hang1
1Department of Computer Science, City University of Hong Kong, Hong Kong, China.
Entropy (Basel, Switzerland)
|June 28, 2023
概括
本综述探讨了在Big Code上训练的大型语言模型 (LLM) 如何增强人工智能辅助编程. 这些模型改进了代码生成和缺陷检测等任务,简化了软件开发.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 软件工程 软件工程 软件工程
背景情况:
- 自然语言处理 (NLP) 技术,特别是基于变压器的大型语言模型 (LLM),在人工智能辅助编程中越来越重要.
- 在大代码上接受培训的法学士,将软件的自然性纳入,像GitHub Copilot和DeepMind AlphaCode这样的强大应用程序.
- 这些模型是各种编程任务的组成部分,包括代码生成,完成,翻译,改进,总结和缺陷/克隆检测.
研究的目的:
- 提供关于NLP和LLM在人工智能辅助编程中的应用的全面文献综述.
- 概述主要的LLM及其在软件开发中的下游应用.
- 探索整合NLP与软件自然性的挑战和机遇,以增强编码辅助.
主要方法:
- 在人工智能辅助编程中对NLP技术和基于变压器的LLM进行文献综述.
- 在代码生成,完成,翻译,改进,总结,缺陷检测和克隆检测中分析LLM应用程序.
- 探索挑战和机遇,包括扩展功能,以移动开发环境,如Xcode.
主要成果:
- 在大代码上接受培训的LLM显著提高了AI辅助编程任务.
- 像GitHub Copilot这样的应用程序证明了这些模型的实际影响.
- 结合NLP与软件自然性,为开发者赋权提供了巨大的机会.
结论:
- 在大代码上接受培训的LLM对人工智能辅助编程具有变革性.
- 对NLP集成的进一步研究可以解锁高级编码辅助,并简化开发.
- 将这些功能扩展到像Xcode这样的移动开发平台,为未来提供了重大机会.
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