一句话：提出面向 AI/ML 赋能软件的结构化开发过程，以弥补现有实践零散、质量与兼容性不足的问题。
许多依赖 AI/ML 模型的软件组织仍采用临时、非结构化的开发实践，模型与周边软件常被分离开发，进而带来模型质量、嵌入系统兼容性以及系统与问题域适配性等问题。已有研究虽改进了若干孤立阶段，但仍缺少面向质量的完整开发框架。该博士研究旨在提出一个增强的 AI/ML 赋能软件开发过程，弥补上述空白，强调方法学严谨性，并支持交付可靠、高质量的 AI/ML 系统。摘要未报告具体评估结果。
Most organizations developing software that depends on AI/ML models often rely on ad hoc or unstructured development practices. In many cases, the model and the surrounding software are developed separately, leading to several problems—particularly regarding the quality of the model, the compatibility of the system in which the model is embedded, and the overall suitability of the resulting system to the problem domain. Although prior research has addressed specific aspects of this process and proposed improvements for isolated phases, a comprehensive, quality-oriented development framework is still lacking. This doctoral research aims to propose an enhanced development process that addresses these gaps and opportunities, ensuring both methodological rigor and the delivery of reliable, high-quality AI/ML-enabled systems.