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English(EN) Benchmarking LLM Agents for Behaviorally Adequate Test Generation from Software Requirements

新的基准测试揭示LLM代理在生成行为充分的测试方面存在困难

研究人员推出了Req2Test-Bench,这是一个旨在评估大型语言模型代理从软件需求生成行为充分的测试的能力的新基准。该基准包含来自八个开源项目的超过5000个执行验证实例,将Gherkin格式的自然语言需求与其对应的开发人员编写的测试实现配对。初步评估显示,在生成可成功执行的测试和行为充分的测试之间存在显著差距,尽管执行成功率为82.8%,但性能最佳的配置的行为充分性仅达到33.8%。 AI

影响 突出了LLM代理在软件测试能力方面的一个关键差距,表明当前模型在没有人工监督的情况下不足以生成行为充分的测试。

排序理由 该集群包含一篇介绍LLM代理评估新基准的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的基准测试揭示LLM代理在生成行为充分的测试方面存在困难

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Tool
该集群包含一篇介绍LLM代理评估新基准的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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Topics
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Binhang Qi, Yiming Liu, Yun Lin, Yangjie Zhou, Hailong Sun, Jin Song Dong ·

    基准测试LLM智能体以从软件需求生成行为充分的测试用例

    arXiv:2610.36584v1 Announce Type: cross Abstract: As large language model agents increasingly compile natural-language requirements into repository-level code, requirement-to-test generation grows in importance to provide validation constraints (or harness) to guide code generati…