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July 2, 2026Proceedings of the ACM on software engineering.Open Access

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

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Authors

MLMinh Le-AnhHNHuyen NguyenATAn Khanh Tran

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Overview

Randomized trial demonstrates improved code generation in repository settings, highlighting dependency awareness.

Key Points

  • The aim is to improve code generation performance within repositories by rethinking how code is treated.
  • Introduced a structure-aware indexing strategy representing code as hierarchical trees.
  • Developed a lightweight dependency-aware retriever to identify relevant dependencies.
  • Combined dependency-aware retrieval with traditional similarity-based methods.
  • Hydra achieved a greater than 5% improvement in Pass@1 compared to the strongest baseline.
  • Smaller models performed comparably to larger models using traditional retrieval methods.
  • State-of-the-art performance established on DevEval and RepoExec benchmarks.

Cite This Study

Le-Anh et al. (2026) studied this question.

synapsesocial.com/papers/6a4600489ed13430313107a9https://doi.org/10.1145/3797144
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Evaluating Retrieval-Augmented Generation Strategies for Repository-Level Code Understanding2026
  2. 2Enhancing Repository-Level Code Generation with Integrated Contextual Information2024 · 3 citations
  3. 3Repoformer: Selective Retrieval for Repository-Level Code Completion2024 · 3 citations
  4. 4Class-Level Code Generation from Natural Language Using Iterative, Tool-Enhanced Reasoning over Repository2024 · 2 citations
  5. 5One Size Does Not Fit All: Revisiting Code Context Engineering for Repository-Level Code Generation2026