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arXiv · 2602.01655

ProjDevBench: Benchmarking AI Coding Agents on End-to-End Project Development

Pengrui Lu·Shiqi Zhang·Yunzhong Hou·Lyumanshan Ye·Chaoyi Huang·Zixi Chen·Ji Zeng·Hantao Jiang·Pengfei Liu·Yiwei Wang·Ming-Hsuan Yang
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Abstract

Recent coding agents can generate complete codebases from simple prompts, yet existing evaluations focus on issue-level bug fixing and lag behind end-to-end development. We introduce ProjDevBench, an end-to-end benchmark that provides project requirements to coding agents and evaluates the resulting repositories. Combining Online Judge (OJ) testing with LLM-assisted code review, the benchmark evaluates agents on (1) system architecture design, (2) functional correctness, and (3) iterative solution refinement. We curate 20 programming problems across 8 categories, covering both concept-oriented tasks and real-world application scenarios, and evaluate six coding agents built on different LLM backends. Our evaluation reports an overall acceptance rate of 27.38%: agents handle basic functionality and data structures but struggle with complex system design, time complexity optimization, and resource management. Our benchmark is available at https://github.com/zsworld6/projdevbench.

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Pengrui Lu, Shiqi Zhang, Yunzhong Hou, Lyumanshan Ye, Chaoyi Huang, Zixi Chen, Ji Zeng, Hantao Jiang, Pengfei Liu, Yiwei Wang, Ming-Hsuan Yang. 2026-02-02. ProjDevBench: Benchmarking AI Coding Agents on End-to-End Project Development. https://arxiv.org/abs/2602.01655

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