Enhancing LLM-Based Coding Tools through Native Integration of IDE-Derived Static Context
<|reference_start|>Enhancing LLM-Based Coding Tools through Native Integration of IDE-Derived Static Context: 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.<|reference_end|>
@article{li2024enhancing,
    title={Enhancing LLM-Based Coding Tools through Native Integration of
  IDE-Derived Static Context},
    author={Yichen Li and Yun Peng and Yintong Huo and Michael R. Lyu},
    journal={arXiv preprint arXiv:2402.03630},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.03630},
    primaryClass={cs.SE cs.AI}
}