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ReACC: A Retrieval-Augmented Code Completion Framework

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arxiv 2203.07722 v1 pith:YCDHTONT submitted 2022-03-15 cs.SE cs.AIcs.CL

Shuai Lu , Nan Duan , Hojae Han , Daya Guo , Seung-won Hwang , Alexey Svyatkovskiy This is my paper · ORCID
classification cs.SEcs.AIcs.CL
keywords codecompletioncontextlanguageapproachcopyingframeworkimprove
verification ladder T0 review T1 audit T2 compute T3 formal
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Code completion, which aims to predict the following code token(s) according to the code context, can improve the productivity of software development. Recent work has proved that statistical language modeling with transformers can greatly improve the performance in the code completion task via learning from large-scale source code datasets. However, current approaches focus only on code context within the file or project, i.e. internal context. Our distinction is utilizing "external" context, inspired by human behaviors of copying from the related code snippets when writing code. Specifically, we propose a retrieval-augmented code completion framework, leveraging both lexical copying and referring to code with similar semantics by retrieval. We adopt a stage-wise training approach that combines a source code retriever and an auto-regressive language model for programming language. We evaluate our approach in the code completion task in Python and Java programming languages, achieving a state-of-the-art performance on CodeXGLUE benchmark.

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Cited by 9 Pith papers

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

  1. 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.

  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. Retrieval-Augmented Code Review Comment Generation

    cs.SE 2025-06 conditional novelty 6.0 of 10

    Retrieval-augmented conditioning on similar code-review pairs improves review comment generation over generation-only baselines, with larger gains for low-frequency tokens, though improvements over retrieval-only base...

  4. Knowledge-Enhanced Program Repair for Data Science Code

    cs.SE 2025-02 conditional novelty 6.0 of 10

    DSrepair combines a knowledge graph of data science APIs with AST-level bug localization to repair LLM-generated code, fixing more DS-1000 tasks than five baseline repair methods.

  5. 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.

  6. Knowledge Matters: Injecting Project and Testing Knowledge into LLM-based Unit Test Generation

    cs.SE 2025-11 conditional novelty 5.0 of 10

    KTester improves LLM-generated Java unit tests on a 110-method benchmark by injecting static project knowledge and decoupling test-case design from test-method generation, beating four baselines.

  7. SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer

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    A retrieval-augmented LLM assistant with iterative error correction nearly doubles the rate of generating executable ParaView visualization scripts compared with unassisted models.

  9. SACL: Understanding and Combating Textual Bias in Code Retrieval with Semantic-Augmented Reranking and Localization

    cs.CL 2025-06 reject novelty 5.0 of 10

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