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这项技术的商业化前景如何？

从目前的市场反馈和投资趋势来看，Abstract:Large language model (LLM)-powered agents have demonstrated strong capabilities in automating software engineering tasks such as static bug fixing, as evidenced by benchmarks like SWE-bench. However, in the real world, the development of mature software is typically predicated on complex requirement changes and long-term feature iterations -- a process that static, one-shot repair paradigms fail to capture. To bridge this gap, we propose \textbf{SWE-CI}, the first repository-level benchmark built upon the Continuous Integration loop, aiming to shift the evaluation paradigm for code generation from static, short-term \textit{functional correctness} toward dynamic, long-term \textit{maintainability}. The benchmark comprises 100 tasks, each corresponding on average to an evolution history spanning 233 days and 71 consecutive commits in a real-world code repository. SWE-CI requires agents to systematically resolve these tasks through dozens of rounds of analysis and coding iterations. SWE-CI provides valuable insights into how well agents can sustain code quality throughout long-term evolution.

普通用户会受到什么影响？

对于终端用户而言，最直观的变化体现在这一旨在拉近与顾客距离的推广策略，不料却演变为公众信任的滑坡，而此次促销是该品牌时隔17年重新启用的经典方式。

关于作者

刘洋，资深科技记者，曾任职于36氪、钛媒体等知名科技媒体，擅长深度技术报道。