  Automated program repair is the task of automatically repairing software
bugs. A promising direction in this field is self-supervised learning, a
learning paradigm in which repair models are trained without commits
representing pairs of bug/fix. In self-supervised neural program repair, those
bug/fix pairs are generated in some ways. The main problem is to generate
interesting and diverse pairs that maximize the effectiveness of training. As a
contribution to this problem, we propose to use back-translation, a technique
coming from neural machine translation. We devise and implement MUFIN, a
back-translation training technique for program repair, with specifically
designed code critics to select high-quality training samples. Our results show
that MUFIN's back-translation loop generates valuable training samples in a
fully automated, self-supervised manner, generating more than half-a-million
pairs of bug/fix. The code critic design is key because of a fundamental
trade-off between how restrictive a critic is and how many samples are
available for optimization during back-translation.
