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New SWE-Shift benchmark reveals coding agents struggle with mid-execution changes

By PulseAugur Editorial · [1 sources] ·

A new benchmark called SWE-Shift has been developed to evaluate coding agents' ability to handle mid-execution changes in software development environments. The benchmark includes 120 scenarios across 12 change families, testing agents on their capacity to complete tasks, preserve existing work, and adapt to updates. Initial evaluations of 17 coding agents revealed that all systems performed significantly worse on tasks with mid-execution changes compared to unchanged environments, with GPT-6 Astra dropping from 67.35% success to 24.79%. AI

IMPACT Highlights a critical gap in current coding agent capabilities, potentially driving development towards more robust and adaptable AI assistants for software development.

RANK_REASON Research paper introducing a new benchmark for evaluating AI coding 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-Shift benchmark reveals coding agents struggle with mid-execution changes

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Research paper introducing a new benchmark for evaluating AI coding 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) · Victor Shea-Jay Huang, Rongqing Li, Zhangyue Yin, Kelong Mao, Yaqi Zhao, Xinyuan Wang, Yao Yin, Lirui Luo, Renrui Zhang, Hongsheng Li ·

    SWE-Shift: Evaluating Coding Agents in Evolving Workspaces

    arXiv:2610.32196v1 Announce Type: new Abstract: Repository-level coding benchmarks largely assume that tasks and environments remain unchanged throughout execution. In software development, code, dependencies, runtime state, and requirements can change while an agent is still wor…

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