  Building defect prediction models based on online learning can enhance
prediction accuracy. It continuously rebuilds a new prediction model, when a
new data point is added. However, a module predicted as "non-defective" can
result in fewer test cases for such modules. Thus, a defective module can be
overlooked during testing. The erroneous test results are used as learning data
by online learning, which could negatively affect prediction accuracy. To
suppress the negative influence, we propose to apply a method that fixes the
prediction as positive during the initial stage of online learning.
Additionally, we improved the method to consider the probability of the
overlooking. In our experiment, we demonstrate this negative influence on
prediction accuracy, and the effectiveness of our approach. The results show
that our approach did not negatively affect AUC but significantly improved
recall.
