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Computer Science > Software Engineering

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By Yinxing Xue, Lei Ma, Yuheng Huang
| arxiv.org
As Code Large Language Models (LLMs) become central to modern software engineering, their inherent stochasticity poses significant real-world risks, where even minor errors can lead to severe functional, security, or safety consequences. Reliable automation, therefore, demands the ability to distinguish between confident, well-supported predictions and stochastic guessing.
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[2511.00872] A Comprehensive Empirical Evaluation of Agent Frameworks on Code-centric Software Engineering Tasks

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By Cuifeng Gao, Chunsong Fan, Wenzhang Yang, Yinxing Xue
| arxiv.org
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (700) Issue with article?
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