System verification is a critical process in the development of engineered systems. Engineers gain confidence in the correct functionality of the system by executing system verification. Traditionally, system verification is implemented by conducting a verification strategy (VS) consisting of verification activities (VA). A VS can be generated using industry standards, expert experience, or quantitative-based methods. However, two limitations exist in these previous studies. First, as an essential part of system verification, correction activities (CA) are used to correct system errors or defects identified by VAs. However, CAs are usually simplified and treated as a component associated with VAs instead of independent decisions. Even though this simplification may accelerate the VS design, it results in inferior VSs because the optimization of correction decisions is ignored. Second, current methods have not handled the issue of complex engineered systems. As the number of activities increases, the magnitude of the possible VSs becomes so large that finding the optimal VS is impossible or impractical. Therefore, these limitations leave room for improving the VS design, especially for complex engineered systems. This dissertation presents a joint verification-correction model (JVCM) to address these gaps. The basic idea of this model is to provide an engineering paradigm for complex engineered systems that simultaneously consider decisions about VAs and CAs. The accompanying research problem is to develop a modeling and analysis framework to solve for joint verification-correction strategies (JVCS). This dissertation aims to address them in three steps. First, verification processes (VP) are modeled mathematically to capture the impacts of VAs and CAs. Second, a JVCM with small strategy spaces is established with all conditions of a VP. A modified backward induction method is proposed to solve for an optimal JVCS in small strategy spaces. Third, a UCB-based tree search approach is designed to find near-optimal JVCSs in large strategy spaces. A case study is conducted and analyzed in each step to show the feasibility of the proposed models and methods. / Doctor of Philosophy / System verification is a critical step in the life cycle of system development. It is used to check that a system conforms to its design requirements. Traditionally, system verification is implemented by conducting a verification strategy (VS) consisting of verification activities (VA). A VS can be generated using industry standards, expert experience, or quantitative-based methods. However, two limitations exist in these methods. First, as an essential part of system verification, correction activities (CA) are used to correct system errors or defects identified by VAs. However, CAs are usually simplified and treated as remedial measures that depend on the results of VAs instead of independent decision choices. Even though this simplification may accelerate the VS design, it results in inferior VSs because the optimization of correction decisions is ignored. Second, current methods have not handled the issue of large systems. As the number of activities increases, the total number of possible VSs becomes so large that it is impossible to find the optimal solution. Therefore, these limitations leave room for improving the VS design, especially for large systems. This dissertation presents a joint verification-correction model (JVCM) to address these gaps. The basic idea of this model is to provide a paradigm for large systems that simultaneously consider decisions about VAs and CAs. The accompanying research problem is to develop a modeling and analysis framework to solve for joint verification-correction strategies (JVCS). This dissertation aims to address them in three steps. First, verification processes (VP) are modeled mathematically to capture the impacts of VAs and CAs. Second, a JVCM with small strategy spaces is established with all conditions of a VP. A modified backward induction method is proposed to solve for an optimal JVCS in small strategy spaces. Third, a UCB-based tree search approach is designed to find near-optimal JVCSs in large strategy spaces. A case study is conducted and analyzed in each step to show the feasibility of the proposed models and methods.