@inproceedings{liu-etal-2025-projecteval,
title = "{P}roject{E}val: A Benchmark for Programming Agents Automated Evaluation on Project-Level Code Generation",
author = "Liu, Kaiyuan and
Pan, Youcheng and
Xiang, Yang and
He, Daojing and
Li, Jing and
Du, Yexing and
Gao, Tianrun",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1036/",
doi = "10.18653/v1/2025.findings-acl.1036",
pages = "20205--20221",
ISBN = "979-8-89176-256-5",
abstract = "Recently, LLM agents have made rapid progress in improving their programming capabilities. However, existing benchmarks lack the ability to automatically evaluate from users' perspective, and also lack the explainability of the results of LLM agents' code generation capabilities. Thus, we introduce ProjectEval, a new benchmark for LLM agents project-level code generation{'}s automated evaluation by simulating user interaction. ProjectEval is constructed by LLM with human reviewing. It has three different level inputs of natural languages or code skeletons. ProjectEval can evaluate the generated projects by user interaction simulation for execution, and by code similarity through existing objective indicators. Through ProjectEval, we find that systematic engineering project code, overall understanding of the project and comprehensive analysis capability are the keys for LLM agents to achieve practical projects. Our findings and benchmark provide valuable insights for developing more effective programming agents that can be deployed in future real-world production."
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%0 Conference Proceedings
%T ProjectEval: A Benchmark for Programming Agents Automated Evaluation on Project-Level Code Generation
%A Liu, Kaiyuan
%A Pan, Youcheng
%A Xiang, Yang
%A He, Daojing
%A Li, Jing
%A Du, Yexing
%A Gao, Tianrun
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F liu-etal-2025-projecteval
%X Recently, LLM agents have made rapid progress in improving their programming capabilities. However, existing benchmarks lack the ability to automatically evaluate from users’ perspective, and also lack the explainability of the results of LLM agents’ code generation capabilities. Thus, we introduce ProjectEval, a new benchmark for LLM agents project-level code generation’s automated evaluation by simulating user interaction. ProjectEval is constructed by LLM with human reviewing. It has three different level inputs of natural languages or code skeletons. ProjectEval can evaluate the generated projects by user interaction simulation for execution, and by code similarity through existing objective indicators. Through ProjectEval, we find that systematic engineering project code, overall understanding of the project and comprehensive analysis capability are the keys for LLM agents to achieve practical projects. Our findings and benchmark provide valuable insights for developing more effective programming agents that can be deployed in future real-world production.
%R 10.18653/v1/2025.findings-acl.1036
%U https://aclanthology.org/2025.findings-acl.1036/
%U https://doi.org/10.18653/v1/2025.findings-acl.1036
%P 20205-20221
Markdown (Informal)
[ProjectEval: A Benchmark for Programming Agents Automated Evaluation on Project-Level Code Generation](https://aclanthology.org/2025.findings-acl.1036/) (Liu et al., Findings 2025)
ACL