Completion by Comprehension: Guiding Code Generation with Multi-Granularity Understanding
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908693750087680 |
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| author | Zhao, Xinkui Liu, Rongkai Zhang, Yifan Zhi, Chen Zhang, Lufei Cheng, Guanjie Xu, Yueshen Deng, Shuiguang Yin, Jianwei |
| author_facet | Zhao, Xinkui Liu, Rongkai Zhang, Yifan Zhi, Chen Zhang, Lufei Cheng, Guanjie Xu, Yueshen Deng, Shuiguang Yin, Jianwei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04538 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Completion by Comprehension: Guiding Code Generation with Multi-Granularity Understanding Zhao, Xinkui Liu, Rongkai Zhang, Yifan Zhi, Chen Zhang, Lufei Cheng, Guanjie Xu, Yueshen Deng, Shuiguang Yin, Jianwei Software Engineering 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. |
| title | Completion by Comprehension: Guiding Code Generation with Multi-Granularity Understanding |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2512.04538 |