  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.
