  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.
