{"ID":2832868,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.04538","arxiv_id":"2512.04538","title":"Completion by Comprehension: Guiding Code Generation with Multi-Granularity Understanding","abstract":"As code completion task from function-level to repository-level, leveraging contextual information from large-scale codebases becomes a core challenge. However, existing retrieval-augmented generation (RAG) methods typically treat code as plain natural language, relying primarily on shallow semantic matching while overlooking structural semantics and code-specific dependencies. This limits their ability to capture control flow and underlying intent, ultimately constraining the quality of generated code. Therefore, we propose CoCo, a novel framework that enables code Completion by Comprehension of multi-granularity context from large-scale code repositories. CoCo employs static code analysis to extract structured context at the function, file, and project levels, capturing execution logic and semantic dependencies. It then adopts an graph-based multi-granularity context selection mechanism to filter out redundant information and remove noise. Consequently, the information is converted into natural language in a consistent manner, thereby functioning as explicit contextual prompts to guide subsequent code completion. Additionally, a structure-aware code re-ranker mechanism ensures alignment at both semantic and structural levels. Extensive experiments on CrossCodeEval and RepoEval benchmarks demonstrate that CoCo consistently surpasses state-of-the-art baselines, achieving up to 20.2% gains in EM. Moreover, the framework is model-agnostic and can be seamlessly integrated into existing methods, leading to significant performance.","short_abstract":"As code completion task from function-level to repository-level, leveraging contextual information from large-scale codebases becomes a core challenge. However, existing retrieval-augmented generation (RAG) methods typically treat code as plain natural language, relying primarily on shallow semantic matching while over...","url_abs":"https://arxiv.org/abs/2512.04538","url_pdf":"https://arxiv.org/pdf/2512.04538v1","authors":"[\"Xinkui Zhao\",\"Rongkai Liu\",\"Yifan Zhang\",\"Chen Zhi\",\"Lufei Zhang\",\"Guanjie Cheng\",\"Yueshen Xu\",\"Shuiguang Deng\",\"Jianwei Yin\"]","published":"2025-12-04T07:37:59Z","proceeding":"cs.SE","tasks":"[\"cs.SE\"]","methods":"[\"RAG\"]","has_code":false}