一句话：从实证软件工程视角刻画 AI 制品生命周期并建模质量决定因素，进而提供决策支持工具。
开源平台如 Hugging Face 已成为 AI 核心基础设施，托管大量机器学习模型、大语言模型、数据集和流水线。然而，这些 AI 制品如何随时间演化、哪些因素驱动其质量，以及如何可靠、可维护、可持续地工程化，仍缺乏系统认识。本博士论文采用实证软件工程视角，结合大规模仓库挖掘、纵向分析、受控实验与工具构建，旨在刻画 AI 制品的生命周期与演化，识别并建模可维护性、可演化性和能源效率等关键质量属性的决定因素，并将洞见转化为面向实践者的决策支持框架与工具，为质量感知的 AI 制品工程提供实证基础。
Open-source platforms such as Hugging Face have become central infrastructure for Artificial Intelligence (AI), hosting large collections of machine learning (ML) models, large language models (LLMs), datasets, and pipelines. These ecosystems enable rapid reuse and experimentation, but little is known about how AI artifacts evolve over time, which factors drive their quality, and how to engineer them in a reliable, maintainable, and sustainable way. This doctoral thesis adopts an empirical Software Engineering (SE) perspective to: (i) characterize the lifecycle and evolution of AI artifacts in large-scale repositories; (ii) identify and model the determinants of key quality attributes, including maintainability, evolvability, and energy efficiency; and (iii) turn these insights into decision-support frameworks and tools for practitioners in AI-based systems. The thesis combines large-scale repository mining, longitudinal analyses, controlled experiments, and tool-building. Overall, it aims to offer an empirically grounded foundation for engineering, evaluating, and supporting quality-aware AI artifacts across their lifecycle.