RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems
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Cited by 99 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Large Language Models (LLMs) have greatly advanced code auto-completion systems, with a potential for substantial productivity enhancements for developers. However, current benchmarks mainly focus on single-file tasks, leaving an assessment gap for more complex, real-world, multi-file programming scenarios. To fill this gap, we introduce RepoBench, a new benchmark specifically designed for evaluating repository-level code auto-completion systems. RepoBench supports both Python and Java and consists of three interconnected evaluation tasks: RepoBench-R (Retrieval), RepoBench-C (Code Completion), and RepoBench-P (Pipeline). Each task respectively measures the system's ability to retrieve the most relevant code snippets from other files as cross-file context, predict the next line of code with cross-file and in-file context, and handle complex tasks that require a combination of both retrieval and next-line prediction. RepoBench aims to facilitate a more complete comparison of performance and encouraging continuous improvement in auto-completion systems. RepoBench is publicly available at https://github.com/Leolty/repobench.
Key facts. Cited by 346 papers in the map (data through 7 Oct 2026). Builds on 33 of its 49 references that are in the map. First posted on arXiv 5 Jun 2023.
Builds on
33 of 49 references.
References · 33
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- 2023Unlimiformer: Long-Range Transformers with Unlimited Length Inputcited by 127
- 2022Repository-Level Prompt Generation for Large Language Models of Codecited by 106
- 2023CoLT5: Faster Long-Range Transformers with Conditional Computationcited by 84
- 2022CoCoMIC: Code Completion By Jointly Modeling In-file and Cross-file Contextcited by 64
- 2020Multi-task Learning based Pre-trained Language Model for Code Completioncited by 58
- 2023Enriching Source Code with Contextual Data for Code Completion Models: An Empirical Studycited by 7
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Citing papers per year: 2023: 9; 2024: 86; 2025: 112; 2026: 139.
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