研究人员开发了一个名为FGDM的新框架，用于检测和修复软件错误。这个多智能体系统利用具有思维链和思维树提示的大型语言模型（LLMs）来理解代码依赖关系。该框架将代码转换为流程图，识别错误并生成修复方案，并与FAISS向量数据库集成以检索过去的类似问题。在C和Python的100多个程序上进行的实验表明，FGDM的性能优于现有方法，显著降低了Levenshtein距离并提高了余弦相似度。 AI
影响 引入了一个新颖的多智能体LLM框架，改进了自动软件错误检测和修复。
排序理由 这是一篇详细介绍软件错误检测和修复新颖框架的研究论文。
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arXiv:2604.24831v1 Announce Type: cross Abstract: Deep Learning methods are becoming prominent in automated software bug detection; however, they lack the global understanding of the given code. Consequently, their performance tends to degrade, especially when they are applied to…
Deep Learning methods are becoming prominent in automated software bug detection; however, they lack the global understanding of the given code. Consequently, their performance tends to degrade, especially when they are applied to large interconnected code bases or complex modula…