Large Language Models (LLMs) have shown impressive capabilities in code generation for popular programming languages. However, their performance on Low-Resource Programming Languages (LRPLs) and Domain-Specific Languages (DSLs) remains a significant challenge, affecting millions of developers-3.5 million users in Rust alone-who cannot fully utilize LLM capabilities. LRPLs and DSLs encounter unique obstacles, including data scarcity and, for DSLs, specialized syntax that is poorly represented in general-purpose datasets. Addressing these challenges is crucial, as LRPLs and DSLs enhance development efficiency in specialized domains, such as finance and science. While several surveys discuss LLMs in software engineering, none focus specifically on the challenges and opportunities associated with LRPLs and DSLs. Our survey fills this gap by systematically reviewing the current state, methodologies, and challenges in leveraging LLMs for code generation in these languages. We filtered 111 papers from over 27,000 published studies between 2020 and 2024 to evaluate the capabilities and limitations of LLMs in LRPLs and DSLs. We report the LLMs used, benchmarks, and metrics for evaluation, strategies for enhancing performance, and methods for dataset collection and curation. We identified four main evaluation techniques and several metrics for assessing code generation in LRPLs and DSLs. Our analysis categorizes improvement methods into six groups and summarizes novel architectures proposed by researchers. Despite various techniques and metrics, a standard approach and benchmark dataset for evaluating code generation in LRPLs and DSLs are lacking. This survey serves as a resource for researchers and practitioners at the intersection of LLMs, software engineering, and specialized programming languages, laying the groundwork for future advancements in code generation for LRPLs and DSLs.

翻译：大语言模型（LLMs）在主流编程语言的代码生成方面已展现出卓越能力。然而，其在低资源编程语言（LRPLs）和领域特定语言（DSLs）上的表现仍面临重大挑战——仅Rust就有350万用户无法充分利用LLM能力。LRPLs和DSLs面临独特障碍：包括数据稀缺性，以及DSLs特有的、在通用数据集中表征不足的专业语法。解决这些挑战至关重要，因为LRPLs和DSLs能提升金融、科学等专业领域的开发效率。现有若干综述探讨了LLMs在软件工程中的应用，但尚未有研究专门聚焦LRPLs和DSLs相关的挑战与机遇。本综述通过系统梳理LLMs在这些语言中代码生成的现状、方法学与挑战填补了这一空白。我们从2020至2024年间发表的27,000余篇研究中筛选出111篇论文，以评估LLMs在LRPLs和DSLs中的能力与局限。我们报告了所使用的LLMs、评估基准与指标、性能提升策略，以及数据集收集与构建方法。我们识别出四种主要评估技术及若干适用于LRPLs和DSLs代码生成的评估指标。本分析将改进方法归纳为六类，并总结了研究者提出的新颖架构。尽管存在多种技术与指标，目前仍缺乏评估LRPLs和DSLs代码生成的标准方法与基准数据集。本综述为LLMs、软件工程与专业编程语言交叉领域的研究者与实践者提供了资源，为LRPLs和DSLs代码生成的未来发展奠定了基础。