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English(EN) SWEPatchTest: Evaluating Software Tests Beyond Reproduction

新的基准和方法增强了AI代码生成评估

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新的研究论文介绍了用于评估和改进程序生成任务中AI模型的新颖方法。第一篇论文SWEPatchTest提出了一个基准，用于评估编码代理生成软件问题正确补丁的能力，揭示了当前评估方法的重大局限性。第二篇论文HERO提出了一种技术，该技术利用执行反馈来优化提示并在测试时改进生成的代码，从而提高了在文本到SQL和文本到Python基准上的性能。 AI

影响 这些进展可以通过改进衡量AI编码助手性能的方式以及它们如何从执行反馈中学习，从而带来更可靠、更高效的AI编码助手。

排序理由 两篇学术论文介绍了用于AI程序生成的新基准和方法。

在 arXiv cs.AI 阅读 →

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

新的基准和方法增强了AI代码生成评估

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两篇学术论文介绍了用于AI程序生成的新基准和方法。
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Topics
paper, product
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Hua Lu, Pengcheng Wang, Han Li, Zichen Tian, Yanyu Chen, Huang He, Bingjin Chen ·

    SWEPatchTest: 超越复现的软件测试评估

    arXiv:2610.18004v1 Announce Type: cross Abstract: Coding agents use generated tests to decide whether a proposed patch satisfies an issue's requirements. A suite that fails on the buggy program and passes after applying the reference patch can still accept an incorrect patch or r…

  2. arXiv cs.CL TIER_1 English(EN) · Mingyu Lu, Timothy Ossowski, Tristan Naumann, Hoifung Poon, Peniel Argaw ·

    HERO：在程序生成中利用证据进行测试时精炼和提示优化

    arXiv:2610.19184v1 Announce Type: new Abstract: Harness optimization improves LLMs by refining reusable prompts and scaffolding, yet requires further adaptation for program generation tasks such as text-to-SQL and text-to-Python. Existing methods often rely on LLM reflectors to p…

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