This book explores the world of reconfigurable stochastic Petri nets (RSPNs), a powerful method for modeling and verifying complex, dynamic and reconfigurable systems. As modern discrete-event systems become increasingly flexible, requiring structural adaptability at runtime, classical Petri nets are proving insufficient. This book presents innovative extensions to Petri nets, offering enhanced modeling capabilities for reconfigurable systems, while ensuring efficient verification. Through a structured approach, this book introduces reconfigurable generalized stochastic Petri nets (RecGSPNs), an advanced framework that integrates reconfigurability while preserving crucial system properties such as liveness, boundedness and deadlock-freedom. This book systematically explores modeling techniques, including stochastic reward nets and dynamic topology transformations, demonstrating their effectiveness through quantitative and qualitative analyses. By addressing challenges in state-space explosion and computational complexity, this book provides essential methodologies for researchers and practitioners working on reconfigurable systems, and serves as a valuable resource for those working in network security, manufacturing systems and distributed computing, where dynamic reconfigurations are essential.