  Automated Program Repair (APR) is defined as the process of fixing a
bug/defect in the source code, by an automated tool. APR tools have recently
experienced promising results by leveraging state-of-the-art Neural Language
Processing (NLP) techniques. APR tools such as TFix and CodeXGLUE combine
text-to-text transformers with software-specific techniques are outperforming
alternatives, these days. However, in most APR studies the train and test sets
are chosen from the same set of projects. In reality, however, APR models are
meant to be generalizable to new and different projects. Therefore, there is a
potential threat that reported APR models with high effectiveness perform
poorly when the characteristics of the new project or its bugs are different
than the training set's(Domain Shift).
  In this study, we first define and measure the domain shift problem in
automated program repair. Then, we then propose a domain adaptation framework
that can adapt an APR model for a given target project. We conduct an empirical
study with three domain adaptation methods FullFineTuning,
TuningWithLightWeightAdapterLayers, and CurriculumLearning using two
state-of-the-art domain adaptation tools (TFix and CodeXGLUE) and two APR
models on 611 bugs from 19 projects. The results show that our proposed
framework can improve the effectiveness of TFix by 13.05% and CodeXGLUE by
23.4%. Another contribution of this study is the proposal of a data synthesis
method to address the lack of labelled data in APR. We leverage transformers to
create a bug generator model. We use the generated synthetic data to domain
adapt TFix and CodeXGLUE on the projects with no data (Zero-shot learning),
which results in an average improvement of 5.76% and 24.42% for TFix and
CodeXGLUE, respectively.
