The process of software defect prediction (SDP) involves predicting which software system modules or components pose the highest risk of being defective. The projections and discernments derived from SDP can then assist the software development team in effectively allocating its finite resources toward potentially susceptible defective modules. Because of this, SDP models need to be improved and refined continuously. Hence, this research proposes the deployment of a cascade generalization (CG) function to enhance the predictive performances of machine learning (ML)-based SDP models. The CG function extends the initial sample space by introducing new samples into the neighbourhood of the distribution function generated by the base classification algorithm, subsequently mitigating its bias. Experiments were conducted to investigate the effectiveness of CG-based Na\"ive Bayes (NB), Decision Tree (DT), and k-Nearest Neighbor (kNN) models on NASA software defect datasets. Based on the experimental results, the CG-based models (CG-NB, CG-DT, CG-kNN) were superior in prediction performance when compared with the baseline NB, DT, and kNN models respectively. Accordingly, the average accuracy value of CG-NB, CG-DT, and CG-kNN models increased by +11.06%, +3.91%, and +5.14%, respectively, over baseline NB, DT, and kNN models. A similar performance was observed for the area under the curve (AUC) value with CG-NB, CG-DT, and CG-kNN recording an average AUC value of +7.98%, +26%, and +24.9% improvement over the baseline NB, DT, and kNN respectively. In addition, the suggested CG-based models outperformed the Bagging and Boosting ensemble variants of the NB, DT, and kNN models as well as existing computationally diverse SDP models.

翻译：软件缺陷预测（SDP）旨在预测软件系统中哪些模块或组件最有可能存在缺陷。SDP得出的预测与洞察可协助软件开发团队有效地将有限资源分配到可能存在缺陷的易感模块上。因此，需要持续改进和完善SDP模型。为此，本研究提出部署级联泛化（CG）函数，以提升基于机器学习（ML）的SDP模型的预测性能。CG函数通过向基分类算法生成的分布函数邻域引入新样本，从而扩展初始样本空间，进而减轻其偏差。本研究通过实验探究了基于CG的朴素贝叶斯（NB）、决策树（DT）和k近邻（kNN）模型在NASA软件缺陷数据集上的有效性。实验结果表明，与基准的NB、DT和kNN模型相比，基于CG的模型（CG-NB、CG-DT、CG-kNN）在预测性能上均表现更优。具体而言，CG-NB、CG-DT和CG-kNN模型的平均准确率较基准NB、DT和kNN模型分别提升了+11.06%、+3.91%和+5.14%。在曲线下面积（AUC）指标上也观察到类似的性能提升：CG-NB、CG-DT和CG-kNN模型的平均AUC值较基准NB、DT和kNN模型分别提高了+7.98%、+26%和+24.9%。此外，所提出的基于CG的模型也优于NB、DT和kNN模型的Bagging与Boosting集成变体，以及现有的多种计算型SDP模型。