Fed-OLF: Federated Oversampling Learning Framework for Imbalanced Software Defect Prediction Under Privacy Protection

Software defect prediction technology can discover potential errors or hidden defects by establishing prediction models before the use of products in the field of software engineering, so as to reduce subsequent problems and improve software quality and security. However, building predictive models requires enough software defect dataset support, especially defect samples. Due to the involvement of confidential information from various organizations or enterprises, software defect data cannot be shared and effectively utilized. Therefore, to achieve collaborative training of multiparty shared software defect prediction models while keeping the data local to various organizations, we made the federated learning framework for the issue of software defect prediction. Meanwhile, the nondefect and defect instances in software defect datasets are usually imbalanced, which can seriously affect the software defect prediction performance of the model. Therefore, this study designs a novel federated oversampling learning framework Fed-OLF. First, the TabDiT method based on deep generative model is proposed in Fed-OLF to expand and rebalance the local imbalanced software defect dataset of each client with a certain degree of privacy protection. Second, a parameter aggregation strategy based on local information entropy is proposed in Fed-OLF to further optimize the parameter aggregation effect of the global shared model, thereby achieving better model performance. We conduct extensive experiments on the PROMISE dataset and the NASA Promise repository, and experimental results on the PROMISE dataset and the NASA Promise repository show that, the proposed Fed-OLF exhibits better predictive performance under the F1-score, G-mean, and AUC metrics when compared with the advanced baseline methods. In addition, we verify that both the TabDiT method and the parameter aggregation strategy based on local information entropy in Fed-OLF are useful, and the combination of them can more effectively improve model performance.