发表机构
European XFEL（欧洲X射线自由电子激光设施）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对欧洲XFEL科学家分析复杂数据面临的挑战，设计并评估智能AI系统。采用设计科学研究方法，经多项研究开发并评估两原型，确定关键挑战，得出对AI代理的要求及设计建议，为开发可维护的AI支持提供指导。
AI 中文摘要
欧洲XFEL的科学家进行的实验会生成非常大且复杂的数据集。后续的数据分析具有挑战性，因为科学家必须将其领域专业知识与分散在文档、工具和支持渠道中的特定于设施和软件的知识相结合。为了解决这个问题，我们设计并评估了一个针对科学家需求定制并与欧洲XFEL的高性能计算环境集成的智能人工智能系统。我们采用设计科学研究方法，进行了快速文献综述、对16个人工智能工具的系统评估、多次访谈、焦点小组讨论以及与欧洲XFEL的专家进行用户研究，以开发和评估两个原型。我们的研究确定了科学数据分析中的关键知识挑战，得出了对支持知识检索和源代码生成的人工智能代理的要求，并为适应不断发展的人工智能工具格局的专门系统提出了设计建议。这些发现为在高度专业化的科学环境中开发可维护的人工智能支持提供了指导。
英文摘要
Scientists at European XFEL conduct experiments that generate very large and complex datasets. The subsequent data analysis is challenging as scientists must combine their domain expertise with facility- and software-specific knowledge scattered across documentation, tools, and support channels. To address this problem, we designed and evaluated an agentic AI system tailored to the scientists' needs and integrated with the high-performance computing environment of European XFEL. Using a design science research approach, we conducted a rapid literature review, a systematic evaluation of 16 AI tools, multiple interviews, a focus group, and a user study with experts at European XFEL to develop and evaluate two prototypes. Our study identifies key knowledge challenges in scientific data analysis, derives requirements for an AI agent that supports knowledge retrieval and source code generation, and proposes design recommendations for a specialized system adaptable to the evolving AI tool landscape. These findings provide guidance for developing maintainable AI support in highly specialized scientific environments.
CommentsAccepted for publication at the 42nd IEEE International Conference on Software Maintenance and Evolution
