The quality of software significantly influences its safety and security. With the rapid expansion of software development, the issue of coding quality has become increasingly critical. The manual and resource-intensive nature of error detection in software and its inherent unreliability underscores the necessity for automation. Consequently, a burgeoning interest is employing machine learning methods for software defect prediction. This study introduces a novel stacking software cross-project defect prediction model. Each weak classifier undergoes a learning process incorporating individual data balancing and feature selection techniques. The efficacy of the model was evaluated using accuracy and F1 score metrics on multiple project datasets sourced from the PROMISE repository. The application of the proposed model yielded a classification accuracy of 0.839 and an F1 score of 0.909, surpassing the average performance of single classifiers.