ProjectEval: A Benchmark for Programming Agents Automated Evaluation on Project-Level Code Generation

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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Publication Details

Published
2025-01-01
DOI
https://doi.org/10.18653/v1/2025.findings-acl.1036
Citations
1
Primary Topic
Model-Driven Software Engineering Techniques
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article
Field-Weighted Citation Impact
2.31
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article

ProjectEval: A Benchmark for Programming Agents Automated Evaluation on Project-Level Code Generation

Youcheng Pan, Daojing He, Jing Li, Yang Xiang et al.
1 citations
Model-Driven Software Engineering Techniques
2.31
article

ProjectEval: A Benchmark for Programming Agents Automated Evaluation on Project-Level Code Generation

Youcheng Pan, Daojing He, Jing Li, Yang Xiang, Yexing Du, Kaiyuan Liu, Tianrun Gao
article en
1 citations

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.

Openalex Percentile: Top 11%
Model-Driven Software Engineering Techniques
2.31
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