  Code review is a fundamental process in software development that plays a
critical role in ensuring code quality and reducing the likelihood of errors
and bugs. However, code review might be complex, subjective, and
time-consuming. Comment generation and code refinement are two key tasks of
this process and their automation has traditionally been addressed separately
in the literature using different approaches. In this paper, we propose a novel
deep-learning architecture, DISCOREV, based on cross-task knowledge
distillation that addresses these two tasks simultaneously. In our approach,
the fine-tuning of the comment generation model is guided by the code
refinement model. We implemented this guidance using two strategies,
feedback-based learning objective and embedding alignment objective. We
evaluated our approach based on cross-task knowledge distillation by comparing
it to the state-of-the-art methods that are based on independent training and
fine-tuning. Our results show that our approach generates better review
comments as measured by the BLEU score.
