Large Language Models (LLMs) have achieved remarkable success in code completion, as evidenced by their essential roles in developing code assistant services such as Copilot. Being trained on in-file contexts, current LLMs are quite effective in completing code for single source files. However, it is challenging for them to conduct repository-level code completion for large software projects that require cross-file information. Existing research on LLM-based repository-level code completion identifies and integrates cross-file contexts, but it suffers from low accuracy and limited context length of LLMs. In this paper, we argue that Integrated Development Environments (IDEs) can provide direct, accurate and real-time cross-file information for repository-level code completion. We propose IDECoder, a practical framework that leverages IDE native static contexts for cross-context construction and diagnosis results for self-refinement. IDECoder utilizes the rich cross-context information available in IDEs to enhance the capabilities of LLMs of repository-level code completion. We conducted preliminary experiments to validate the performance of IDECoder and observed that this synergy represents a promising trend for future exploration.

翻译：大型语言模型（LLMs）在代码补全方面取得了显著成功，其在Copilot等代码助手服务开发中的关键作用即是明证。由于基于文件内上下文进行训练，当前LLMs在处理单一源文件的代码补全时相当有效。然而，对于需要跨文件信息的大型软件项目，它们难以进行仓库级别的代码补全。现有基于LLM的仓库级代码补全研究虽能识别并整合跨文件上下文，但存在准确率低及LLM上下文长度受限的问题。本文提出，集成开发环境（IDE）可为仓库级代码补全提供直接、准确且实时的跨文件信息。我们设计了IDECoder这一实用框架，利用IDE原生静态上下文进行跨上下文构建，并借助诊断结果实现自我优化。IDECoder通过利用IDE中丰富的跨上下文信息，增强了LLMs在仓库级代码补全中的能力。初步实验验证了IDECoder的性能，表明这种协同作用代表了未来探索中一个颇具前景的方向。