ReviewRanker: A Semi-Supervised Learning Based Approach for Code Review Quality Estimation
Saifullah Mahbub, Md. Easin Arafat, Chowdhury Rafeed Rahman, Zannatul Ferdows, Masum Hasan · 2024
Inspection of code 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. ReviewRanker has the potential of minimizing the back-and-forth cycle existing in the development and review process. Related code and data can be found at: https://github.com/saifarnab/code_review