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An undersampling method for software defect prediction based on Hilbert curve mapping distance
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AI
DOI：10.1016/j.engappai.2025.112519.png)
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Abstract
En 中文
The class imbalance problem presents a significant challenge in software defect prediction. The undersampling method enhances prediction performance by eliminating non-defective instances, thereby enabling the model to focus more on defective instances. However, the effective selection of representative non-defective instances while preserving the overall data distribution remains a critical challenge. Inspired by the space-filling property of Hilbert curves, we propose the Hilbert Curve Mapping Distance Undersampling (HCMDU) method for software defect prediction. This method first maps instances to Hamming space to ensure that similar instances are positioned closer together in the space. Instance circular domains are then partitioned based on the Hamming distance between them, which facilitates the exploration of instance variability within a localized region. Finally, the Hilbert curve mapping distance is employed to further uncover the data distribution pattern within the instance circular domains. The experimental results demonstrate that HCMDU delivers outstanding performance across 16 randomly selected software defect datasets in both Random Forest (RF) and Classification and Regression Trees (CART). Moreover, the results are further corroborated by the Friedman ranking and Nemenyi post-hoc test, which indicate that HCMDU significantly improves the performance of software defect prediction.
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