  Software bugs are common and correcting them accounts for a significant part
of costs in the software development and maintenance process. This calls for
automatic techniques to deal with them. One promising direction towards this
goal is gaining repair knowledge from historical bug fixing examples.
Retrieving insights from software development history is particularly appealing
with the constant progress of machine learning paradigms and skyrocketing `big'
bug fixing data generated through Continuous Integration (CI). In this paper,
we present R-Hero, a novel software repair bot that applies continual learning
to acquire bug fixing strategies from continuous streams of source code
changes, implemented for the single development platform Github/Travis CI. We
describe R-Hero, our novel system for learning how to fix bugs based on
continual training, and we uncover initial successes as well as novel research
challenges for the community.
