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English(EN) FeatureLoop: Benchmarking Coding Agents under Evolving Requirements

新基准显示编码代理难以应对不断变化的软件功能

作者 PulseAugur 编辑部 · [2 个来源] ·

引入了两个新基准 FeatureArc 和 FeatureLoop，用于评估编码代理在处理不断变化的软件功能和需求方面的能力。FeatureArc 包含 73 个任务，发现代理在初始请求上表现成功，但在功能后期阶段进行测试时，成功率会显著下降，继承的缺陷是一个常见问题。FeatureLoop 包含 61 个任务，显示即使是表现最好的模型也难以在几次纠正性修订中从错误中恢复，这凸显了在可靠、低干预开发方面面临的挑战。这两个基准都强调了当前编码代理在没有人为监督的情况下，在多个开发阶段保持正确性和功能性的难度。 AI

影响 这些基准突显了当前 AI 编码代理在处理迭代开发和不断变化的需求方面存在重大局限性，表明需要进一步研究以保持代码的长期正确性。

排序理由 两篇学术论文介绍了用于评估 AI 编码代理的新基准。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新基准显示编码代理难以应对不断变化的软件功能

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两篇学术论文介绍了用于评估 AI 编码代理的新基准。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Yunlong Wang, Alyssa Wu, Cuiyun Gao, Jia Feng, Songyuanyi Lu, Zhichao Hu ·

    FeatureArc：在不断演进的功能上对编码代理进行基准测试

    arXiv:2610.37434v1 Announce Type: cross Abstract: A software feature usually evolves over time: developers extend its first version as new use cases and requirements arrive, and each extension builds on code written earlier. Most feature benchmarks evaluate a single request on a …

  2. arXiv cs.LG TIER_1 English(EN) · Jia Feng, Alyssa Wu, Cuiyun Gao, Yunlong Wang, Songyuanyi Lu, Zhichao Hu ·

    FeatureLoop：在不断变化的需求下对编码代理进行基准测试

    arXiv:2610.35488v1 Announce Type: cross Abstract: Software development with coding agents often begins with a rough request that users refine after trying the implementation. Final correctness alone does not capture the repeated guidance this process requires. We introduce Featur…

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