Completion by Comprehension: Guiding Code Generation with Multi-Granularity Understanding

• URL: http://arxiv.org/abs/2512.04538v1
• Date: Thu, 04 Dec 2025 07:37:59 GMT
• Title: Completion by Comprehension: Guiding Code Generation with Multi-Granularity Understanding
• Authors: Xinkui Zhao, Rongkai Liu, Yifan Zhang, Chen Zhi, Lufei Zhang, Guanjie Cheng, Yueshen Xu, Shuiguang Deng, Jianwei Yin,
• Abstract summary: CoCo is a novel framework that enables code Completion by of multi-granularity context from large-scale code repositories.<n>Experiments on CrossCodeEval and RepoEval benchmarks demonstrate that CoCo consistently surpasses state-of-the-art baselines.
• Score: 37.78627994991325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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