Computer experiments have been widely used in practice as important supplements to traditional physical experiments in studying complex processes. However, a computer experiment is expensive in terms of its computational time. Hence, Gaussian process (GP) was proposed to be used as a statistical surrogate. The scope of the research is rather broad: we are concerned with design and analysis of computer experiments based on a GP. We use comprehensive assessment strategies to evaluate the effect of several factors on the prediction accuracy of the GP model. In addition, we propose new methods motivated by the assessment. Working with an engineering computer model, we also provide insights into issues faced by practitioners.