Prospect theory-based oversampling for software defect prediction

In software defect prediction, the data difficulty factors have a more significant impact on prediction performance than class imbalance.However, most existing oversampling methods ignore the data difficulty factors inherent in software project datasets when addressing the class imbalance problem, which leads to poor prediction performance. To solve above problems, a Prospect theory-based Over Sampling algorithm (POS) for software defect prediction was proposed, which evaluated the learning difficulty of minority samples by considering the influence of homogeneous and heterogeneous samples within the local neighborhood. To be specific, POS constructed homogeneous gains and heterogeneous losses to characterize the prospect value of minority samples via a gravity-based strategy, and strengthened heterogeneous losses to calculate the sampling weights of minority samples for reducing the risk of introducing data difficulty factors, improving the quality of synthetic samples, and further improving the prediction performance. Experimental results on the NASA datasets showed that POS outperformed the comparison algorithms in terms of performance metrics AUC, balance and G-mean. © 2024 CIMS. All rights reserved.