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

翻译：对话式人工智能系统将基于人工智能的解决方案与会话界面的灵活性相结合。然而，现有的大多数测试方案并未直接适应对话式交互的特性或人工智能组件的行为。为应对这一局限性，本博士论文研究了一类新的对话式人工智能系统测试方法，重点在于从语言与人工智能组件之间的集成，到单个对话代理，直至对话式人工智能系统的多代理实现，在不同粒度级别上验证其构成要素。