  Although peer code review is widely adopted in both commercial and open
source development, existing studies suggest that such code reviews often
contain a significant amount of non-useful review comments. Unfortunately, to
date, no tools or techniques exist that can provide automatic support in
improving those non-useful comments. In this paper, we first report a
comparative study between useful and non-useful review comments where we
contrast between them using their textual characteristics, and reviewers'
experience. Then, based on the findings from the study, we develop RevHelper, a
prediction model that can help the developers improve their code review
comments through automatic prediction of their usefulness during review
submission. Comparative study using 1,116 review comments suggested that useful
comments share more vocabulary with the changed code, contain salient items
like relevant code elements, and their reviewers are generally more
experienced. Experiments using 1,482 review comments report that our model can
predict comment usefulness with 66\% prediction accuracy which is promising.
Comparison with three variants of a baseline model using a case study validates
our empirical findings and demonstrates the potential of our model.
