  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.
