Do Not Treat Code as Natural Language: Implications for Repository-Level Code Generation and Beyond

• URL: http://arxiv.org/abs/2602.11671v1
• Date: Thu, 12 Feb 2026 07:44:00 GMT
• Title: Do Not Treat Code as Natural Language: Implications for Repository-Level Code Generation and Beyond
• Authors: Minh Le-Anh, Huyen Nguyen, Khanh An Tran, Nam Le Hai, Linh Ngo Van, Nghi D. Q. Bui, Bach Le,
• Abstract summary: We present Hydra, a repository-level code generation framework that treats code as structured code rather than natural language.<n>We show that Hydra achieves state-of-the-art performance across open- and closed-source CodeLLMs.
• Score: 13.550121154853715
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large language models for code (CodeLLMs) have demonstrated remarkable success in standalone code completion and generation, sometimes even surpassing human performance, yet their effectiveness diminishes in repository-level settings where cross-file dependencies and structural context are essential. Existing Retrieval-Augmented Generation (RAG) approaches often borrow strategies from NLP, relying on chunking-based indexing and similarity-based retrieval. Chunking results in the loss of coherence between code units and overlooks structural relationships, while similarity-driven methods frequently miss functionally relevant dependencies such as helper functions, classes, or global variables. To address these limitations, we present Hydra, a repository-level code generation framework that treats code as structured code rather than natural language. Our approach introduces (i) a structure-aware indexing strategy that represents repositories as hierarchical trees of functions, classes, and variables, preserving code structure and dependencies, (ii) a lightweight dependency-aware retriever (DAR) that explicitly identifies and retrieves the true dependencies required by a target function, and (iii) a hybrid retrieval mechanism that combines DAR with similarity-based retrieval to provide both essential building blocks and practical usage examples. Extensive experiments on the challenging DevEval and RepoExec benchmarks, both requiring function implementation from real-world repositories with complex large repository context, show that Hydra achieves state-of-the-art performance across open- and closed-source CodeLLMs. Notably, our method establishes a new state of the art in repository-level code generation, surpassing strongest baseline by over 5% in Pass@1 and even enabling smaller models to match or exceed the performance of much larger ones that rely on existing retrievers.

Related papers

• RealSec-bench: A Benchmark for Evaluating Secure Code Generation in Real-World Repositories [58.32028251925354]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation, but their proficiency in producing secure code remains a critical, under-explored area.<n>We introduce RealSec-bench, a new benchmark for secure code generation meticulously constructed from real-world, high-risk Java repositories.
  arXiv  Detail & Related papers  (2026-01-30T08:29:01Z)
• AlignCoder: Aligning Retrieval with Target Intent for Repository-Level Code Completion [55.21541958868449]
  We propose AlignCoder, a repository-level code completion framework.<n>Our framework generates an enhanced query that bridges the semantic gap between the initial query and the target code.<n>We employ reinforcement learning to train an AlignRetriever that learns to leverage inference information in the enhanced query for more accurate retrieval.
  arXiv  Detail & Related papers  (2026-01-27T15:23:14Z)
• In Line with Context: Repository-Level Code Generation via Context Inlining [11.065371614078723]
  In this paper, we introduce InlineCoder, a novel framework for repository-level code generation.<n>InlineCoder enhances the understanding of repository context by inlining the unfinished function into its call graph.
  arXiv  Detail & Related papers  (2026-01-01T15:56:24Z)
• Completion by Comprehension: Guiding Code Generation with Multi-Granularity Understanding [37.78627994991325]
  CoCo is a novel framework that enables code Completion by of multi-granularity context from large-scale code repositories.<n>Experiments on CrossCodeEval and RepoEval benchmarks demonstrate that CoCo consistently surpasses state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-12-04T07:37:59Z)
• RepoScope: Leveraging Call Chain-Aware Multi-View Context for Repository-Level Code Generation [16.544483144957407]
  RepoScope is a call chain-aware multi-view context for repository-level code generation.<n>We present a novel call chain prediction method that utilizes the repository's structural semantics to improve the identification of callees in the target function.<n>RepoScope outperforms state-of-the-art methods, achieving up to a 36.35% relative improvement in pass@1 scores.
  arXiv  Detail & Related papers  (2025-07-20T02:35:36Z)
• EpiCoder: Encompassing Diversity and Complexity in Code Generation [66.43738008739555]
  Existing methods for code generation use code snippets as seed data.<n>We introduce a novel feature tree-based synthesis framework, which revolves around hierarchical code features.<n>Our framework provides precise control over the complexity of the generated code, enabling functionalities that range from function-level operations to multi-file scenarios.
  arXiv  Detail & Related papers  (2025-01-08T18:58:15Z)
• CodeRAG-Bench: Can Retrieval Augment Code Generation? [78.37076502395699]
  We conduct a systematic, large-scale analysis of code generation using retrieval-augmented generation.<n>We first curate a comprehensive evaluation benchmark, CodeRAG-Bench, encompassing three categories of code generation tasks.<n>We examine top-performing models on CodeRAG-Bench by providing contexts retrieved from one or multiple sources.
  arXiv  Detail & Related papers  (2024-06-20T16:59:52Z)
• On the Impacts of Contexts on Repository-Level Code Generation [5.641402231731082]
  We present RepoExec, a novel benchmark designed to evaluate repository-level code generation.<n>We focus on three key aspects: executability, functional correctness through comprehensive test case generation, and accurate utilization of cross-file contexts.
  arXiv  Detail & Related papers  (2024-06-17T10:45:22Z)
• Class-Level Code Generation from Natural Language Using Iterative, Tool-Enhanced Reasoning over Repository [4.767858874370881]
  We introduce RepoClassBench, a benchmark designed to rigorously evaluate LLMs in generating class-level code within real-world repositories. RepoClassBench includes "Natural Language to Class generation" tasks across Java, Python & C# from a selection of repositories. We introduce Retrieve-Repotools-Reflect (RRR), a novel approach that equips LLMs with static analysis tools to iteratively navigate & reason about repository-level context.
  arXiv  Detail & Related papers  (2024-04-22T03:52:54Z)
• RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation [96.75695811963242]
  RepoCoder is a framework to streamline the repository-level code completion process. It incorporates a similarity-based retriever and a pre-trained code language model. It consistently outperforms the vanilla retrieval-augmented code completion approach.
  arXiv  Detail & Related papers  (2023-03-22T13:54:46Z)
• Autoregressive Search Engines: Generating Substrings as Document Identifiers [53.0729058170278]
  Autoregressive language models are emerging as the de-facto standard for generating answers. Previous work has explored ways to partition the search space into hierarchical structures. In this work we propose an alternative that doesn't force any structure in the search space: using all ngrams in a passage as its possible identifiers.
  arXiv  Detail & Related papers  (2022-04-22T10:45:01Z)

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.