During code reviews, software developers often raise security concerns if they find any. Ignoring such concerns can bring a severe impact on the performance of a software product. This risk can be reduced if we can automatically identify such code reviews that trigger security concerns so that we can perform additional scrutiny from the security experts. Therefore, the objective of this study is to develop an automated tool to identify code reviews that trigger security concerns.

With this goal, I developed an approach named ASTOR, where I combine two separate deep learning-based classifiers– (i) using code review comments and (ii) using the corresponding code context, and make an ensemble using Logistic Regression. Based on stratified ten-fold cross-validation, the best ensemble model achieves the F1-score of 79.7% with an accuracy of 88.4% to automatically identify code reviews that raise security concerns.