  Code review is considered a key process in the software industry for
minimizing bugs and improving code quality. Inspection of review process
effectiveness and continuous improvement can boost development productivity.
Such inspection is a time-consuming and human-bias-prone task. We propose a
semi-supervised learning based system ReviewRanker which is aimed at assigning
each code review a confidence score which is expected to resonate with the
quality of the review. Our proposed method is trained based on simple and and
well defined labels provided by developers. The labeling task requires little
to no effort from the developers and has an indirect relation to the end goal
(assignment of review confidence score). ReviewRanker is expected to improve
industry-wide code review quality inspection through reducing human bias and
effort required for such task. The system has the potential of minimizing the
back-and-forth cycle existing in the development and review process. Usable
code and dataset for this research can be found at:
https://github.com/saifarnab/code_review

Software Engineering