RepoHyper: Search-Expand-Refine on Semantic Graphs for Repository-Level Code Completion
<|reference_start|>RepoHyper: Search-Expand-Refine on Semantic Graphs for Repository-Level Code Completion: 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.<|reference_end|>
@article{phan2024repohyper:,
    title={RepoHyper: Search-Expand-Refine on Semantic Graphs for Repository-Level
  Code Completion},
    author={Huy N. Phan, Hoang N. Phan, Tien N. Nguyen, Nghi D. Q. Bui},
    journal={arXiv preprint arXiv:2403.06095},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06095},
    primaryClass={cs.SE cs.AI}
}