GraphCoder: Enhancing Repository-Level Code Completion via Code Context Graph-based Retrieval and Language Model

• URL: http://arxiv.org/abs/2406.07003v2
• Date: Fri, 13 Sep 2024 07:19:16 GMT
• Title: GraphCoder: Enhancing Repository-Level Code Completion via Code Context Graph-based Retrieval and Language Model
• Authors: Wei Liu, Ailun Yu, Daoguang Zan, Bo Shen, Wei Zhang, Haiyan Zhao, Zhi Jin, Qianxiang Wang,
• Abstract summary: GraphCoder is a retrieval-augmented code completion framework. It uses general code knowledge and the repository-specific knowledge via a graph-based retrieval-generation process. It achieves higher exact match (EM) on average, with increases of +6.06 in code match and +6.23 in identifier match, while using less time and space.
• Score: 30.625128161499195
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The performance of repository-level code completion depends upon the effective leverage of both general and repository-specific knowledge. Despite the impressive capability of code LLMs in general code completion tasks, they often exhibit less satisfactory performance on repository-level completion due to the lack of repository-specific knowledge in these LLMs. To address this problem, we propose GraphCoder, a retrieval-augmented code completion framework that leverages LLMs' general code knowledge and the repository-specific knowledge via a graph-based retrieval-generation process. In particular, GraphCoder captures the context of completion target more accurately through code context graph (CCG) that consists of control-flow, data- and control-dependence between code statements, a more structured way to capture the completion target context than the sequence-based context used in existing retrieval-augmented approaches; based on CCG, GraphCoder further employs a coarse-to-fine retrieval process to locate context-similar code snippets with the completion target from the current repository. Experimental results demonstrate both the effectiveness and efficiency of GraphCoder: Compared to baseline retrieval-augmented methods, GraphCoder achieves higher exact match (EM) on average, with increases of +6.06 in code match and +6.23 in identifier match, while using less time and space.

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