  Real bug fixes found in open source repositories seem to be the perfect
source for learning to localize and repair real bugs. However, the absence of
large scale bug fix collections has made it difficult to effectively exploit
real bug fixes in the training of larger neural models in the past. In
contrast, artificial bugs -- produced by mutating existing source code -- can
be easily obtained at a sufficient scale and are therefore often preferred in
the training of existing approaches. Still, localization and repair models that
are trained on artificial bugs usually underperform when faced with real bugs.
This raises the question whether bug localization and repair models trained on
real bug fixes are more effective in localizing and repairing real bugs.
  We address this question by introducing RealiT, a pre-train-and-fine-tune
approach for effectively learning to localize and repair real bugs from real
bug fixes. RealiT is first pre-trained on a large number of artificial bugs
produced by traditional mutation operators and then fine-tuned on a smaller set
of real bug fixes. Fine-tuning does not require any modifications of the
learning algorithm and hence can be easily adopted in various training
scenarios for bug localization or repair (even when real training data is
scarce). In addition, we found that training on real bug fixes with RealiT is
empirically powerful by nearly doubling the localization performance of an
existing model on real bugs while maintaining or even improving the repair
performance.
