An assessment of the priority level of reported bugs assists in the bug fix scheduling. The bug reports are generated in large numbers, and the manual assessment of bug priority is not feasible. The automation of bug priority assessment needs historical data of the project, but in the case of new projects, historical data are not available to build the classifier. In this situation, training the model on a similar project and testing on the target project is the only solution. The data reported on the bug tracking system may consist of uncertainty. An entropy-based solution has been widely used to handle uncertainty. In this research, we present a cross-project entropy-based bug priority assessment. The performance evaluation has been carried out using different performance metrics, namely “accuracy” and “F-measure.” The proposed model has significant improvement over the existing work available in the literature.