Code validation aims at assessing the uncertainty affecting the predictions of a physical system by using both the outputs of a computer code which attempt to reproduce it and the available field measurements. In the one hand, the codemay be not a perfect representation of the reality. In the other hand, some code parameters can be uncertain and need to be estimated: this issue is referred to as code calibration. After having provided a unified view of the main procedures of code validation, we propose several contributions for solving some issues arising in computer codes which are both costly and considered as black-box functions. First, we develop a Bayesian testing procedure to detect whether or not a discrepancy function, called code discrepancy, has to be taken into account between the code outputs and the physical system. Second, we present new algorithms for building sequential designs of experiments in order to reduce the error occurring in the calibration process based on a Gaussian process emulator. Lastly, a validation procedure of a thermal code is conducted as the preliminary step of a decision problem where an energy supplier has to commit for an overall energy consumption forecast to customers. Based on the Bayesian decision theory, some optimal plug-in estimators are computed.