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ACL Findings2025

DependEval: Benchmarking LLMs for Repository Dependency Understanding

Junjia Du, Yadi Liu, Hongcheng Guo, Jiawei Wang, Haojian Huang, Yunyi Ni, Zhoujun Li

摘要

While large language models (LLMs) have shown considerable promise in code generation, real-world software development demands advanced repository-level reasoning. This includes understanding dependencies, project structures, and managing multi-file changes. However, the ability of LLMs to effectively comprehend and handle complex code repositories has yet to be fully explored. To address challenges, we introduce a hierarchical benchmark designed to evaluate repository dependency understanding (DEPENDEVAL). Benchmark is based on 2683 repositories collected from real-world websites. It evaluates models on three core tasks: Dependency Recognition, Repository Construction, and Multi-file Editing, across 8 programming languages from actual code repositories. Our evaluation of over 25 LLMs reveals substantial performance gaps and provides valuable insights into repositorylevel code understanding 1 * Corresponding author. 1 We provide code and datasets at https://github.com/ink7- sudo/DependEval.

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引用它的顶会 Paper2

  1. EvoEmpirBench: Dynamic Spatial Reasoning with Agent-ExpVerPukun Zhao, Longxiang Wang, Miaowei Wang, Chen Chen 等AAAI 2026 · 2 次引用
  2. Latent Inter-User Difference Modeling for LLM PersonalizationYilun Qiu, Tianhao Shi, Xiaoyan Zhao, Fengbin Zhu 等EMNLP 2025 · 1 次引用

它引用的 Paper9

  1. Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah 等NeurIPS 2020 · 64,255 次引用
  2. CodeBERT: A Pre-Trained Model for Programming and Natural LanguagesZhangyin Feng, Daya Guo, Duyu Tang, Nan Duan 等EMNLP Findings 2020 · 2,828 次引用
  3. Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code GenerationJiawei Liu, Chunqiu Steven Xia, Yuyao Wang, Lingming ZhangNeurIPS 2023 · 2,317 次引用
  4. SWE-bench: Can Language Models Resolve Real-world Github Issues?Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao 等ICLR 2024 · 2,082 次引用
  5. RepoBench: Benchmarking Repository-Level Code Auto-Completion SystemsTianyang Liu, Canwen Xu, Julian J. McAuleyICLR 2024 · 338 次引用

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  3. RepoReasoner: Evaluating Repository-Level Code Reasoning Ability of Long-Context Language ModelsYanlin Wang, Suiquan Wang, Yanli Wang, Bowen Zhang 等FSE 2026
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