Effectiveness of auto-sklearn in Software Bug Prediction

Auto-sklearn is a recent attention-gathering software library for automated machine learning that can au-tomatically select appropriate prediction models and hyper parameters for a given data set. In this paper we empirically evaluate the effectiveness of auto-sklearn in software bug prediction. In the experiment, we used software metrics of 20 OSS projects for inter-version bug prediction and compared auto-sklearn with random forrest, decision tree and linear descriminat analysis by using AUC of ROC curve as a performance measure. As a result, auto-sklearn showed similar prediction performance as random forrest. We conclude that, although auto-sklearn is useful for bug prediction, we cannot expect better prediction performance than conventional modeling techniques.