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

• URL: http://arxiv.org/abs/2602.01655v2
• Date: Mon, 09 Feb 2026 15:17:29 GMT
• Title: ProjDevBench: Benchmarking AI Coding Agents on End-to-End Project Development
• Authors: Pengrui Lu, Shiqi Zhang, Yunzhong Hou, Lyumanshan Ye, Chaoyi Huang, Zixi Chen, Ji Zeng, Hantao Jiang, Pengfei Liu, Yiwei Wang, Ming-Hsuan Yang,
• Abstract summary: ProjDevBench is an end-to-end benchmark that provides project requirements to coding agents and evaluates the resulting repositories.<n>We curate 20 programming problems across 8 categories, covering both concept-oriented tasks and real-world application scenarios.<n>Our evaluation reports an overall acceptance rate of 27.38%: agents handle basic functionality but struggle with complex system design, time optimization, and resource management.
• Score: 49.63491095660809
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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