Testing AI-Based Software Systems: From Theory to Practice

The universal integration of Artificial Intelligence (AI) into various software systems presents unique challenges to traditional software testing methodologies. This paper and accompanying lecture delve into the complexities of testing AI-based software, covering the range from theoretical foundations to practical applications. We will explore the fundamental differences between testing conventional software and AI systems, analyze inherent challenges such as the black-box nature of AI models, data dependency, bias, and evolving behavior. The proposal will detail various theoretical frameworks and practical approaches, including Model-Based Testing (MBT), Data-Driven Testing (DDT), Adversarial Testing, Explainable AI (XAI), etc. Furthermore,   it   will   highlight   the   role   of   AI-powered   tools   in   enhancing   the   testing process and discuss best practices for effective AI testing, drawing insights from case studies.   The   session   aims   to   provide   a   comprehensive   understanding   of   current methodologies and future trends in this critical and rapidly evolving field.