Building defect prediction models based on online learning can enhance prediction accuracy. It continuously rebuilds a new prediction model when adding a new data point. However, predicting a module as "non-defective" (i.e., negative prediction) can result in fewer test cases for such modules. Therefore, defects can be overlooked during testing, even when the module is defective. The erroneous test results are used as learning data by online learning, which could negatively affect prediction accuracy. In our experiment, we demonstrate this negative influence on prediction accuracy.

翻译：基于在线学习构建缺陷预测模型可提升预测精度。当添加新数据点时，该模型会持续重建新的预测模型。然而，将模块预测为"无缺陷"（即负向预测）会导致此类模块的测试用例减少。因此，即使模块存在缺陷，测试过程中也可能忽视该缺陷。这些错误的测试结果被在线学习用作训练数据，从而可能对预测精度产生负面影响。在我们的实验中，我们验证了这种对预测精度的负面影响。