Within-project and cross-project defect prediction based on model averaging.

In contrast, the comparisons between MA and NaiveBayes ( p = 0.0574), MA and XGBoost ( p = 0.0500), and MA and LightGBM ( p = 0.0580) yield marginally significant results.

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In particular, MA shows highly significant improvements over AdaBoost, J48, SMO, LogitBoost, LMT, and RandomForest—with p-values well below the 0.01 threshold—indicating that the superior performance of MA is not due to random chance and is consistently robust across different datasets.

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