Using Machine Learning to Identify Code Fragments for Manual Review

Paper in proceeding, 2020

Code reviews are one of the first quality assurance tasks in continuous software integration and delivery. The goal of our work is to reduce the need for manual reviews by automatically identify which code fragments should be further reviewed manually. We conducted an action research study with two companies where we extracted code reviews and build machine learning classifiers (AdaBoost and Convolutional Neural Network- CNN). Our results show that the accuracy of recognizing code fragments that require manual review, measured with Matthews Correlation Coefficient, was 0.70 in the combination of our own feature extraction and CNN. We conclude that this way of combining automation with manual code reviews can improve the speed of reviews while providing organizations with the possibility to support knowledge transfer among the designers.