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New SWE-Clar benchmark tests AI coding agents on requirement clarification

By PulseAugur Editorial · [1 sources] ·

A new benchmark called SWE-Clar has been developed to evaluate the ability of coding AI agents to clarify incomplete client requirements and deliver greenfield software. The benchmark uses 100 real-world software development tasks from Upwork, each valued at over $4,000, and includes a multi-round interaction phase for requirement clarification followed by code implementation and testing. Experiments with nine frontier models, including GPT-6 Astra, showed that even the best-performing model achieved only a 67% task pass rate, highlighting the difficulty of autonomous software delivery in freelance settings. AI

IMPACT This benchmark could accelerate the development of more capable AI agents for software development by providing a standardized evaluation for requirement clarification.

RANK_REASON The cluster contains an academic paper introducing a new benchmark for AI agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New SWE-Clar benchmark tests AI coding agents on requirement clarification

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The cluster contains an academic paper introducing a new benchmark for AI agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jialei Li, Dingxian Wang, Xiaoyan Zhao, Yimeng Bai, Xuanyi Zhu, Yang Zhang, Chao Yin, Wentao Shi, Ganesh Prabhu, Karthik Gomadam Rajagopal, Brett Levert, Darvin Yi, Andrew Rabinovich, Zhao Chen ·

    SWE-Clar: Can Coding Agents Clarify Incomplete Client Requirements and Deliver Greenfield Software?

    arXiv:2610.31854v1 Announce Type: cross Abstract: Greenfield software development in freelance settings requires coding agents to turn incomplete client requests into working software. This makes requirement clarification essential, yet clients may be unable to provide continuous…

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