Enhancing LLM-Based Coding Tools through Native Integration of IDE-Derived Static Context

• URL: http://arxiv.org/abs/2402.03630v2
• Date: Mon, 19 Feb 2024 06:39:23 GMT
• Title: Enhancing LLM-Based Coding Tools through Native Integration of IDE-Derived Static Context
• Authors: Yichen Li and Yun Peng and Yintong Huo and Michael R. Lyu
• Abstract summary: We argue that Integrated Development Environments (IDEs) can provide direct, accurate and real-time cross-file information for repository-level code completion. We propose a framework that leveragesIDE native static contexts for cross-context construction and diagnosis results for self-refinement.
• Score: 41.91246546266515
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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