Pith. sign in

REVIEW 11 cited by

REPOFUSE: Repository-Level Code Completion with Fused Dual Context

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

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

Ming Liang , Xiaoheng Xie , Gehao Zhang , Xunjin Zheng , Peng Di , wei jiang , Hongwei Chen , Chengpeng Wang
show 1 more author
Gang Fan
This is my paper · ORCID
classification cs.SEcs.AI
keywords codecontextrepofusecompletioninferencerepository-levelaccuracycompletions
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 11 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. Comprendia: AI-Augmented Code Comprehension

    cs.SE 2026-08 conditional novelty 6.0 of 10

    Comprendia's GACP selects callees for LLM prompts using a visible dependency graph, making context inclusion auditable and reproducible across LLM families.

  3. 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.

  4. 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.

  5. Code Graph Model (CGM): A Graph-Integrated Large Language Model for Repository-Level Software Engineering Tasks

    cs.SE 2025-05 conditional novelty 6.0 of 10

    A graph-integrated open-source LLM with agentless RAG resolves 43% of SWE-bench Lite issues, best among open-weight models.

  6. Repository Structure-Aware Training Makes SLMs Better Issue Resolver

    cs.SE 2024-12 conditional novelty 6.0 of 10

    Training 7B code models on automatically constructed repository-localization and code-edit data improves their SWE-Bench-verified resolution rate from about 1% to 7%.

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

    cs.SE 2026-07 conditional novelty 5.0 of 10

    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.

  9. 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.

  10. 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.

  11. Knowledge Augmented Complex Problem Solving with Large Language Models: A Survey

    cs.LG 2025-05 unverdicted novelty 2.0 of 10

    A survey that organizes LLM-based complex problem solving into three components, multi-step reasoning, domain knowledge, and result verification, and reviews methods and challenges across four application domains.

Pith tools