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REPOFUSE: Repository-Level Code Completion with Fused Dual Context

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arxiv 2402.14323 v2 pith:IOD7THKF submitted 2024-02-22 cs.SE cs.AI

REPOFUSE: Repository-Level Code Completion with Fused Dual Context

Ming Liang , Xiaoheng Xie , Gehao Zhang , Xunjin Zheng , Peng Di , wei jiang , Hongwei Chen , Chengpeng Wang
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Gang Fan
This is my paper
classification cs.SE cs.AI
keywords codecontextrepofusecompletioninferencerepository-levelaccuracycompletions
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The success of language models in code assistance has spurred the proposal of repository-level code completion as a means to enhance prediction accuracy, utilizing the context from the entire codebase. However, this amplified context can inadvertently increase inference latency, potentially undermining the developer experience and deterring tool adoption - a challenge we termed the Context-Latency Conundrum. This paper introduces REPOFUSE, a pioneering solution designed to enhance repository-level code completion without the latency trade-off. REPOFUSE uniquely fuses two types of context: the analogy context, rooted in code analogies, and the rationale context, which encompasses in-depth semantic relationships. We propose a novel rank truncated generation (RTG) technique that efficiently condenses these contexts into prompts with restricted size. This enables REPOFUSE to deliver precise code completions while maintaining inference efficiency. Through testing with the CrossCodeEval suite, REPOFUSE has demonstrated a significant leap over existing models, achieving a 40.90% to 59.75% increase in exact match (EM) accuracy for code completions and a 26.8% enhancement in inference speed. Beyond experimental validation, REPOFUSE has been integrated into the workflow of a large enterprise, where it actively supports various coding tasks.

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Cited by 8 Pith papers

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

  1. RepoReasoner: Evaluating Repository-Level Code Reasoning Ability of Long-Context Language Models

    cs.SE 2026-07 conditional novelty 7.0

    RepoReasoner is a repository-level code-reasoning benchmark with output-prediction and call-chain tasks; the best LLM reaches only 69.1% Pass@1 even with oracle context, with low recall in dependency tracing.

  2. Is Agentic AI Ready for Real-World Hardware Engineering? A Deep Dive with Phoenix-bench

    cs.AR 2026-05 unverdicted novelty 7.0

    Phoenix-bench shows agentic AI systems lose 37-58% resolved rate when moving from SWE-bench Verified to hardware tasks because bugs spread across parallel modules via signal flow, with testbench feedback lifting perfo...

  3. In Line with Context: Repository-Level Code Generation via Context Inlining

    cs.SE 2026-01 unverdicted novelty 7.0

    InlineCoder reframes repository-level code generation as function-level coding by using a draft anchor to inline the target function into its call graph for upstream usage and downstream dependency context.

  4. When LLMs Lag Behind: Knowledge Conflicts from Evolving APIs in Code Generation

    cs.SE 2026-04 unverdicted novelty 6.0

    LLMs produce executable code only 42.55% of the time under API evolution without full documentation, improving to 66.36% with structured docs and by 11% more with reasoning strategies, yet outdated patterns persist.

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

    cs.SE 2026-01 conditional novelty 6.0

    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.

  6. Beyond "What to Retrieve": Uncertainty in Retrieval-Augmented Code Generation

    cs.SE 2026-07 conditional novelty 5.0

    Uncertainty-aware multi-source retrieval improves GPT repository-level code selection over plain RAG but matches verification-and-repair alone and is backend- and context-dependent.

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

    cs.SE 2025-09 conditional novelty 5.0

    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.

  8. Retrieval-Augmented Generation for AI-Generated Content: A Survey

    cs.CV 2024-02 accept novelty 5.0

    A survey classifying RAG foundations for AIGC, summarizing enhancements, cross-modal applications, benchmarks, limitations, and future directions.