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RepoHyper: Search-Expand-Refine on Semantic Graphs for Repository-Level Code Completion

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arxiv 2403.06095 v4 pith:OOW2UAOI submitted 2024-03-10 cs.SE cs.AI

Huy N. Phan , Hoang N. Phan , Tien N. Nguyen , Nghi D. Q. Bui This is my paper · ORCID
classification cs.SEcs.AI
keywords coderepohypercompletiongraphrepository-levelsemanticcontextrelevant
verification ladder T0 review T1 audit T2 compute T3 formal

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Code Large Language Models (CodeLLMs) have demonstrated impressive proficiency in code completion tasks. However, they often fall short of fully understanding the extensive context of a project repository, such as the intricacies of relevant files and class hierarchies, which can result in less precise completions. To overcome these limitations, we present \tool, a multifaceted framework designed to address the complex challenges associated with repository-level code completion. Central to RepoHYPER is the {\em Repo-level Semantic Graph} (RSG), a novel semantic graph structure that encapsulates the vast context of code repositories. Furthermore, RepoHyper leverages Expand and Refine retrieval method, including a graph expansion and a link prediction algorithm applied to the RSG, enabling the effective retrieval and prioritization of relevant code snippets. Our evaluations show that \tool markedly outperforms existing techniques in repository-level code completion, showcasing enhanced accuracy across various datasets when compared to several strong baselines. Our implementation of RepoHYPER can be found at https://github.com/FSoft-AI4Code/RepoHyper.

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Forward citations

Cited by 10 Pith papers

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

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

    cs.SE 2026-07 conditional novelty 7.0 of 10

    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. Enhancing Project-Specific Code Completion by Inferring Internal API Information

    cs.SE 2025-07 conditional novelty 6.0 of 10

    A retrieval-augmented code completion method that infers project-internal APIs from a rough draft and a static knowledge base, beating existing repo-level baselines.

  3. Risk Assessment Framework for Code LLMs via Leveraging Internal States

    cs.SE 2025-04 conditional novelty 6.0 of 10

    PtTrust pre-trains a sparse autoencoder on LLM hidden states at line boundaries, then trains a small ranker to identify erroneous code lines, reporting state-of-the-art cross-task and cross-language results.

  4. Optimizing Datasets for Code Summarization: Is Code-Comment Coherence Enough?

    cs.SE 2025-02 conditional novelty 6.0 of 10

    Filtering code summarization training data by code-comment coherence gives no better results than random selection, and halving the training set does not hurt performance.

  5. Correctness Assessment of Code Generated by Large Language Models Using Internal Representations

    cs.SE 2025-01 conditional novelty 6.0 of 10

    Internal hidden states of code LLMs carry a usable signal about whether the generated code is correct, and a lightweight probing classifier on those states predicts test-passing with F1 around 0.7 to 0.8 across benchmarks.

  6. Improving FIM Code Completions via Context & Curriculum Based Learning

    cs.IR 2024-12 conditional novelty 6.0 of 10

    Fine-tuning FIM code models on curriculum examples with retrieved context improves completion quality and live acceptance, with the largest gains for small models.

  7. Repository-Level Graph Representation Learning for Enhanced Security Patch Detection

    cs.SE 2024-12 conditional novelty 6.0 of 10

    RepoSPD builds repository-level code-property graphs for security patch detection and reports accuracy improvements of 11.90% and 3.10% over prior baselines on two extended C/C++ datasets.

  8. CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval

    cs.SE 2024-11 conditional novelty 6.0 of 10

    A family of code embedding models (400M to 7B) trained with multi-stage LoRA achieves top CoIR scores and competitive BEIR text scores, improving code RAG.

  9. ContextModule: Improving Code Completion via Repository-level Contextual Information

    cs.SE 2024-12 conditional novelty 5.0 of 10

    Feeding code-completion models a developer's recent file history, similar code, and key symbol definitions raised completion acceptance rates in internal ByteDance deployments by an estimated 15 percent.

  10. Adaptive Request Scheduling for CodeLLM Serving with SLA Guarantees

    cs.SE 2025-06 conditional novelty 4.0 of 10

    SABER uses a fitted scalability curve and deadline-aware admission control to dynamically size continuous batches, improving SLA-compliant throughput by up to 26% over the best static configuration.

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