ProjectEval: A Benchmark for Programming Agents Automated Evaluation on Project-Level Code Generation
March 10, 2025 Β· Declared Dead Β· π Annual Meeting of the Association for Computational Linguistics
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Authors
Kaiyuan Liu, Youcheng Pan, Yang Xiang, Daojing He, Jing Li, Yexing Du, Tianrun Gao
arXiv ID
2503.07010
Category
cs.SE: Software Engineering
Cross-listed
cs.CL
Citations
10
Venue
Annual Meeting of the Association for Computational Linguistics
Last Checked
4 months ago
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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