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

ReviewRanker: A Semi-Supervised Learning Based Approach for Code Review Quality Estimation