The rise of large language models (LLMs) has accelerated the development of automated techniques and tools for supporting various software engineering tasks, e.g., program understanding, code generation, software testing, and program repair. As CodeLLMs are being employed toward automating these tasks, one question that arises, especially in enterprise settings, is whether these coding assistants and the code LLMs that power them are ready for real-world projects and enterprise use cases, and how do they impact the existing software engineering process and user experience. In this paper we survey 57 developers from different domains and with varying software engineering skill about their experience with AI coding assistants and CodeLLMs. We also reviewed 35 user surveys on the usage, experience and expectations of professionals and students using AI coding assistants and CodeLLMs. Based on our study findings and analysis of existing surveys, we discuss the requirements for AI-powered coding assistants.

翻译：大型语言模型（LLM）的兴起加速了支持各类软件工程任务（如程序理解、代码生成、软件测试与程序修复）的自动化技术与工具的发展。随着代码大语言模型（CodeLLM）被应用于这些任务的自动化，尤其在企业环境中，一个重要问题随之产生：这些编码助手及其背后的代码大语言模型是否已为实际项目和企业用例做好准备？它们如何影响现有的软件工程流程与用户体验？本文通过问卷调查了来自不同领域、具备多样化软件工程技能的57名开发者，了解他们使用AI编码助手与代码大语言模型的实际体验。同时，我们系统综述了35份关于专业人员及学生使用AI编码助手与代码大语言模型的使用情况、体验与期望的现有用户调研报告。基于本研究的发现以及对现有调研的分析，我们深入探讨了AI驱动的编码助手所需满足的关键需求。