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Code review is one of the best practices as a powerful safeguard for softwarequality. In practice, senior or highly skilled reviewers inspect source codeand provide constructive comments, considering what authors may ignore, forexample, some special cases. The collaborative validation between contributorsresults in code being highly qualified and less chance of bugs. However, sincepersonal knowledge is limited and varies, the efficiency and effectiveness ofcode review practice are worthy of further improvement. In fact, it still takesa colossal and time-consuming effort to deliver useful review comments. Thispaper explores a synergy of multiple practical review comments to enhance codereview and proposes AUGER (AUtomatically GEnerating Review comments): a reviewcomments generator with pre-training models. We first collect empirical reviewdata from 11 notable Java projects and construct a dataset of 10,882 codechanges. By leveraging Text-to-Text Transfer Transformer (T5) models, theframework synthesizes valuable knowledge in the training stage and effectivelyoutperforms baselines by 37.38% in ROUGE-L. 29% of our automatic reviewcomments are considered useful according to prior studies. The inferencegenerates just in 20 seconds and is also open to training further. Moreover,the performance also gets improved when thoroughly analyzed in case study.

AUGER: Automatically Generating Review Comments with Pre-training Models