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 RepoHyper, a multifaceted framework designed to address the complex challenges associated with repository-level code completion. Central to RepoHyper is the 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 RepoHyper markedly outperforms existing techniques in repository-level code completion, showcasing enhanced accuracy across various datasets when compared to several strong baselines.

Code Large Language Models (CodeLLMs) often lack contextual understanding of project repositories, but the RepoHyper framework addresses this limitation by introducing a Repo-level Semantic Graph (RSG) and leveraging graph expansion and link prediction algorithms to improve repository-level code completion accuracy.

RepoHyper: 软件仓库级别的代码自动完成功能需要更好的上下文检索