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

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arxiv 2406.07003 v2 pith:4MRG7IHI submitted 2024-06-11 cs.SE

Wei Liu , Ailun Yu , Daoguang Zan , Bo Shen , Wei Zhang , Haiyan Zhao , Zhi Jin , Qianxiang Wang This is my paper
classification cs.SE
keywords codecompletiongraphcodercontextknowledgegeneralllmsmatch
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LLM Agents Can See Code Repositories

    cs.SE 2026-06 unverdicted novelty 7.0 of 10

    Visual graphs of repository structure added to text inputs for multimodal LLM agents reduce token consumption by up to 26% while maintaining or improving issue-resolution accuracy.

  2. Function-Aware Fill-in-the-Middle as Mid-Training for Coding Agent Foundation Models

    cs.AI 2026-07 unverdicted novelty 6.0 of 10

    Mid-training on function-aware fill-in-the-middle data improves SWE-Bench by 2.8-5.4 points and reduces post-training capability loss.

  3. Cross-Lingual Token Arbitrage: Optimizing Code Agent Context Windows via Local LLM Preprocessing

    cs.AI 2026-06 unverdicted novelty 6.0 of 10

    A local Llama 3.2 pre-flight rewriter that translates and compacts non-English coding prompts cuts prompt tokens by 34–47% on a new 200-task benchmark while preserving accuracy across three commercial backends.

  4. Vanishing orders, suspensions and zero degree Tur\'an densities

    math.CO 2026-03 unverdicted novelty 6.0 of 10

    Vanishing 2-degree Turán density forces a 2-vanishing vertex order; suspensions transfer zero-density between consecutive degree parameters, so non-classical degree densities accumulate at zero.

  5. SGAgent: Suggestion-Guided LLM-Based Multi-Agent Framework for Repository-Level Software Repair

    cs.SE 2026-02 conditional novelty 6.0 of 10

    A three-agent locate-suggest-fix framework with a knowledge-graph toolkit resolves 154/300 SWE-Bench-Lite issues with Claude-3.5, outperforming same-model baselines by 5-10 points.

  6. Better Call Grep: Evaluating and Improving Grep-Like Lexical Retrieval for Repository-Level Code Completion

    cs.SE 2026-01 conditional novelty 6.0 of 10

    LLM-generated ripgrep queries plus BM25 re-ranking and line-interval de-duplication outperform graph- and RL-based retrievers for repository-level code completion on CrossCodeEval and RepoEval-Updated.

  7. MRCoder: An Efficient Context Selecting Approach for Repository-Level Code Generation

    cs.SE 2026-07 conditional novelty 5.0 of 10

    Draft-guided, API-and-similarity context selection plus parallel draft verification improves repository-level code generation quality while cutting tokens and latency versus RAG and prior compressors.

  8. GRACE: Graph-Guided Repository-Aware Code Completion through Hierarchical Code Fusion

    cs.SE 2025-09 conditional novelty 5.0 of 10

    GRACE combines a multi-level code graph, hybrid text-structure retrieval, and graph fusion to improve repository-level code completion over vanilla and graph-based RAG baselines.

  9. A Deep Dive into Retrieval-Augmented Generation for Code Completion: Experience on WeChat

    cs.SE 2025-07 conditional novelty 5.0 of 10

    On WeChat's closed-source codebase, similarity-based RAG with combined BM25 and GTE-Qwen retrieval improves open-source LLM code completion more than identifier-based retrieval, with gains growing for larger models.

  10. Blueprint First, Model Second: A Framework for Deterministic LLM Workflow

    cs.SE 2025-08 conditional novelty 4.0 of 10

    A source-code-driven 'blueprint first' agent framework reports strong τ-bench results, but the paper contains an abstract/body mismatch and releases no artifacts.

  11. Knowledge Graph Based Repository-Level Code Generation

    cs.AI 2025-05 reject novelty 4.0 of 10

    A knowledge graph code retrieval pipeline is described, but its headline results come from an evaluation that skips the retrieval step and anchors context on the known target function.

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