What to Retrieve for Effective Retrieval-Augmented Code Generation? An Empirical Study and Beyond

• URL: http://arxiv.org/abs/2503.20589v1
• Date: Wed, 26 Mar 2025 14:41:38 GMT
• Title: What to Retrieve for Effective Retrieval-Augmented Code Generation? An Empirical Study and Beyond
• Authors: Wenchao Gu, Juntao Chen, Yanlin Wang, Tianyue Jiang, Xingzhe Li, Mingwei Liu, Xilin Liu, Yuchi Ma, Zibin Zheng,
• Abstract summary: Repository-level code generation remains challenging due to complex code dependencies and the limitations of large language models (LLMs) in processing long contexts.<n>We propose AllianceCoder, a novel context-integrated method that employs chain-of-thought prompting to decompose user queries into implementation steps and retrieves APIs via semantic description matching.<n>Through extensive experiments on CoderEval and RepoExec, AllianceCoder achieves state-of-the-art performance, improving Pass@1 by up to 20% over existing approaches.
• Score: 32.467437657603604
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Repository-level code generation remains challenging due to complex code dependencies and the limitations of large language models (LLMs) in processing long contexts. While retrieval-augmented generation (RAG) frameworks are widely adopted, the effectiveness of different retrieved information sources-contextual code, APIs, and similar snippets-has not been rigorously analyzed. Through an empirical study on two benchmarks, we demonstrate that in-context code and potential API information significantly enhance LLM performance, whereas retrieved similar code often introduces noise, degrading results by up to 15%. Based on the preliminary results, we propose AllianceCoder, a novel context-integrated method that employs chain-of-thought prompting to decompose user queries into implementation steps and retrieves APIs via semantic description matching. Through extensive experiments on CoderEval and RepoExec, AllianceCoder achieves state-of-the-art performance, improving Pass@1 by up to 20% over existing approaches.

Related papers

• EpiCoder: Encompassing Diversity and Complexity in Code Generation [49.170195362149386]
  We introduce a novel feature tree-based synthesis framework inspired by Abstract Syntax Trees (AST) Unlike AST, which captures syntactic structure of code, our framework models semantic relationships between code elements. We fine-tuned widely-used base models to create the EpiCoder series, achieving state-of-the-art performance at both the function and file levels.
  arXiv  Detail & Related papers  (2025-01-08T18:58:15Z)
• GeAR: Generation Augmented Retrieval [82.20696567697016]
  Document retrieval techniques form the foundation for the development of large-scale information systems. The prevailing methodology is to construct a bi-encoder and compute the semantic similarity. We propose a new method called $textbfGe$neration that incorporates well-designed fusion and decoding modules.
  arXiv  Detail & Related papers  (2025-01-06T05:29:00Z)
• CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval [103.116634967815]
  We introduce CodeXEmbed, a family of large-scale code embedding models ranging from 400M to 7B parameters. Our novel training pipeline unifies multiple programming languages and transforms various code-related tasks into a common retrieval framework. Our 7B model sets a new state-of-the-art (SOTA) in code retrieval, outperforming the previous leading model, Voyage-Code, by over 20% on CoIR benchmark.
  arXiv  Detail & Related papers  (2024-11-19T16:54:45Z)
• Context-Augmented Code Generation Using Programming Knowledge Graphs [0.0]
  Large Language Models (LLMs) and Code-LLMs (CLLMs) frequently face difficulties when dealing with challenging and complex problems. We present a novel framework that leverages a Programming Knowledge Graph (PKG) to semantically represent and retrieve code.
  arXiv  Detail & Related papers  (2024-10-09T16:35:41Z)
• DOCE: Finding the Sweet Spot for Execution-Based Code Generation [69.5305729627198]
  We propose a comprehensive framework that includes candidate generation, $n$-best reranking, minimum Bayes risk (MBR) decoding, and self-ging as the core components. Our findings highlight the importance of execution-based methods and the difference gap between execution-based and execution-free methods.
  arXiv  Detail & Related papers  (2024-08-25T07:10:36Z)
• Prompt-based Code Completion via Multi-Retrieval Augmented Generation [15.233727939816388]
  ProCC is a code completion framework leveraging prompt engineering and the contextual multi-armed bandits algorithm. ProCC outperforms state-of-the-art code completion technique by 8.6% on our collected open-source benchmark suite. ProCC also allows augmenting fine-tuned techniques in a plug-and-play manner, yielding 5.6% improvement over our studied fine-tuned model.
  arXiv  Detail & Related papers  (2024-05-13T07:56:15Z)
• Repoformer: Selective Retrieval for Repository-Level Code Completion [30.706277772743615]
  Recent advances in retrieval-augmented generation (RAG) have initiated a new era in repository-level code completion. In this paper, we propose a selective RAG framework to avoid retrieval when unnecessary. We show that our framework is able to accommodate different generation models, retrievers, and programming languages.
  arXiv  Detail & Related papers  (2024-03-15T06:59:43Z)
• StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback [58.20547418182074]
  We introduce StepCoder, a novel framework for code generation, consisting of two main components. CCCS addresses the exploration challenge by breaking the long sequences code generation task into a Curriculum of Code Completion Subtasks. FGO only optimize the model by masking the unexecuted code segments to provide Fine-Grained Optimization. Our method improves the ability to explore the output space and outperforms state-of-the-art approaches in corresponding benchmarks.
  arXiv  Detail & Related papers  (2024-02-02T13:14:31Z)
• REINFOREST: Reinforcing Semantic Code Similarity for Cross-Lingual Code Search Models [11.78036105494679]
  This paper introduces a novel code-to-code search technique that enhances the performance of Large Language Models (LLMs) We present the first-ever code search method that encodes dynamic information during training without the need to execute either the corpus under search or the search query at inference time.
  arXiv  Detail & Related papers  (2023-05-05T20:46:56Z)
• Enhancing Semantic Code Search with Multimodal Contrastive Learning and Soft Data Augmentation [50.14232079160476]
  We propose a new approach with multimodal contrastive learning and soft data augmentation for code search. We conduct extensive experiments to evaluate the effectiveness of our approach on a large-scale dataset with six programming languages.
  arXiv  Detail & Related papers  (2022-04-07T08:49:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.