发表机构
University of Sannio; Smart Shaped s.r.l.（萨尼奥大学; Smart Shaped有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出AIGen，一种基于MLflow框架、结合挖掘启发式方法与大语言模型的模块化AIBoM生成器，可生成符合SPDX 3.0标准的AI构件清单，助力AI供应链治理合规。
AI 中文摘要
负责任地开发和部署人工智能（AI）系统需要对其组成构件进行严格的文档记录，例如数据集、模型权重、训练流水线和运行时依赖项。尽管软件包数据交换（SPDX）3.0标准引入了对AI和数据集配置文件的原生支持，但能够以自动化、可扩展方式生成符合标准的AI物料清单（AIBoM）的实用工具仍然稀缺。本文提出了AIGen，这是一种模块化的AIBoM生成器，可生成符合SPDX 3.0 AI配置文件的机器可读、可互操作的AI系统构件清单。AIGen基于MLflow MLOps框架运行，结合了挖掘启发式方法与大型语言模型来生成AIBoM。插件接口允许从业者扩展该工具，添加特定领域的收集器而无需修改核心代码库，支持Hugging Face、PyTorch和TensorFlow等异构AI框架。AIGen旨在促进符合欧盟AI法案、美国国家标准与技术研究院（NIST）AI风险管理框架以及ISO/IEC 42001标准，为透明、可问责的AI供应链治理提供具体、可重复使用的基础。工具URL：this https URL 工具视频：this https URL _nAbXDWfVL4
英文摘要
The responsible development and deployment of artificial intelligence (AI) systems requires rigorous documentation of their constituent artifacts, e.g., datasets, model weights, training pipelines, and runtime dependencies. Although the Software Package Data Exchange (SPDX) 3.0 standard introduced native support for AI and dataset profiles, practical tooling capable of generating standards-compliant AI Bills of Materials (AIBoMs) in an automated and extensible manner remains scarce. This paper presents AIGen, a modular AIBoM generator that produces machine-readable, interoperable inventories of AI system components that comply with the SPDX 3.0 AI profile. AIGen works on top of the MLflow MLOps framework and combines mining heuristics with Large Language Models to generate AIBoMs. A plugin interface allows practitioners to extend the tool with domain-specific collectors without modifying the core codebase, supporting heterogeneous AI frameworks such as Hugging Face, PyTorch, and TensorFlow. AIGen is designed to facilitate compliance with the European Union AI Act, the NIST AI Risk Management Framework, and ISO/IEC 42001, providing a concrete, reusable foundation for transparent, accountable AI supply chain governance. Tool URL: https://github.com/danielebifolco/AIGen Tool Video: https://youtu.be/\_nAbXDWfVL4
Journal ref41st IEEE/ACM International Conference on Automated Software Engineering (ASE26) Munich, Germany during October 12-16, 2026
