Code Large Language Models (CodeLLMs) have demonstrated impressive proficiency in code completion tasks. However, they often fall short of fully understanding the extensive context of a project repository, such as the intricacies of relevant files and class hierarchies, which can result in less precise completions. To overcome these limitations, we present \tool, a multifaceted framework designed to address the complex challenges associated with repository-level code completion. Central to RepoHYPER is the {\em Repo-level Semantic Graph} (RSG), a novel semantic graph structure that encapsulates the vast context of code repositories. Furthermore, RepoHyper leverages Expand and Refine retrieval method, including a graph expansion and a link prediction algorithm applied to the RSG, enabling the effective retrieval and prioritization of relevant code snippets. Our evaluations show that \tool markedly outperforms existing techniques in repository-level code completion, showcasing enhanced accuracy across various datasets when compared to several strong baselines. Our implementation of RepoHYPER can be found at https://github.com/FSoft-AI4Code/RepoHyper.

翻译：代码大语言模型（CodeLLMs）在代码补全任务中展现出卓越的能力。然而，它们往往难以充分理解项目仓库的广泛上下文，例如相关文件的复杂细节和类层次结构，这可能导致补全结果不够精确。为克服这些局限，我们提出了RepoHyper这一多维度框架，旨在应对仓库级代码补全相关的复杂挑战。RepoHYPER的核心是“仓库级语义图谱”（RSG），这是一种新颖的语义图结构，能够封装代码仓库的广阔上下文。此外，RepoHyper采用扩展与精化检索方法，包括对RSG应用的图扩展算法和链接预测算法，从而实现对相关代码片段的高效检索与优先级排序。评估结果表明，RepoHYPER在仓库级代码补全任务中显著优于现有技术，与多个强基线模型相比，在多个数据集上均展现出更高的准确率。RepoHyper的实现代码已发布于https://github.com/FSoft-AI4Code/RepoHyper。