DependEval: Benchmarking LLMs for Repository Dependency Understanding
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
Authors
- Yadi Liu (ORCID: https://orcid.org/0000-0002-8898-1813)
- Zhoujun Li (ORCID: https://orcid.org/0000-0002-9603-9713)
- Hongcheng Guo (ORCID: https://orcid.org/0000-0001-6957-4158)
- Jiawei Wang (ORCID: https://orcid.org/0000-0003-2627-4897)
- Junping Du (ORCID: https://orcid.org/0000-0001-8590-3767)
- Yu‐Qing Ni (ORCID: https://orcid.org/0000-0002-0321-5571)
- Huabing Huang (ORCID: https://orcid.org/0000-0001-6253-8437)
Publication Details
- Published
- 2025-01-01
- DOI
- https://doi.org/10.18653/v1/2025.findings-acl.373
- Primary Topic
- Software Engineering Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00