一句话：提出面向对话式AI系统的多层次测试方法，覆盖语言与AI组件集成、单个对话代理及多代理实现。
对话式AI系统将基于AI的组件与灵活的对话接口相结合，但现有测试方案难以直接适配对话交互特征和AI组件行为。针对这一局限，本博士论文研究一类新的对话式AI系统测试方法，核心思想是在不同粒度上验证系统构成要素：从语言组件与AI组件之间的集成，到单个对话代理，再到对话式AI系统的多代理实现。论文关注如何为这些层级设计相应的测试策略，以覆盖传统测试未充分处理的对话性与AI行为。摘要未给出具体实验或案例结果，主要贡献在于提出多层次测试的问题框架与方法方向。
Conversational AI systems combine AI-based solutions with the flexibility of conversational interfaces. However, most existing testing solutions do not straightforwardly adapt to the characteristics of conversational interaction or to the behavior of AI components. To address this limitation, this Ph.D. thesis investigates a new family of testing approaches for conversational AI systems, focusing on the validation of their constituent elements at different levels of granularity, from the integration between the language and the AI components, to individual conversational agents, up to multi-agent implementations of conversational AI systems.