提升AI生成代码的安全性与可靠性，让编程更高效可信。
Optimizing AI-Assisted Code Generation
- 通过优化模型输出，增强生成代码的功能性与可执行性
- 提出保障代码安全与高质量的综合策略，适配高要求场景
- 面向开发者与AI研究者，推动编程民主化与技术普惠
近年来，AI辅助代码生成工具显著改变了软件开发方式。尽管这些工具主要用于传统开发，未来将扩展至构建强大且安全的AI系统。以ChatGPT、OpenAI Codex、GitHub Copilot和AlphaCode为代表的代码生成系统，依托大规模语言模型（LLMs）在机器学习（ML）与自然语言处理（NLP）上的进展。然而，这些模型基于概率生成，虽能从自然语言输入生成复杂代码，但无法保证生成代码的功能性与安全性。为充分释放该技术潜力，必须确保生成代码在安全性、可靠性、功能性和质量上达标。本文综述现有实现路径，探讨优化策略，并研究如何使系统生成安全、高性能、可执行的AI模型，同时提升其可访问性，推动AI开发更具包容性与公平性。
原文摘要 · Abstract (English)
In recent years, the rise of AI-assisted code-generation tools has significantly transformed software development. While code generators have mainly been used to support conventional software development, their use will be extended to powerful and secure AI systems. Systems capable of generating code, such as ChatGPT, OpenAI Codex, GitHub Copilot, and AlphaCode, take advantage of advances in machine learning (ML) and natural language processing (NLP) enabled by large language models (LLMs). However, it must be borne in mind that these models work probabilistically, which means that although they can generate complex code from natural language input, there is no guarantee for the functionality and security of the generated code. However, to fully exploit the considerable potential of this technology, the security, reliability, functionality, and quality of the generated code must be guaranteed. This paper examines the implementation of these goals to date and explores strategies to optimize them. In addition, we explore how these systems can be optimized to create safe, high-performance, and executable artificial intelligence (AI) models, and consider how to improve their accessibility to make AI development more inclusive and equitable.
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