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Code review is one of the best practices as a powerful safeguard for software quality. In practice, senior or highly skilled reviewers inspect source code and provide constructive comments, considering what authors may ignore, for example, some special cases. The collaborative validation between contributors results in code being highly qualified and less chance of bugs. However, since personal knowledge is limited and varies, the efficiency and effectiveness of code review practice are worthy of further improvement. In fact, it still takes a colossal and time-consuming effort to deliver useful review comments. This paper explores a synergy of multiple practical review comments to enhance code review and proposes AUGER (AUtomatically GEnerating Review comments): a review comments generator with pre-training models. We first collect empirical review data from 11 notable Java projects and construct a dataset of 10,882 code changes. By leveraging Text-to-Text Transfer Transformer (T5) models, the framework synthesizes valuable knowledge in the training stage and effectively outperforms baselines by 37.38% in ROUGE-L. 29% of our automatic review comments are considered useful according to prior studies. The inference generates just in 20 seconds and is also open to training further. Moreover, the performance also gets improved when thoroughly analyzed in case study., Comment: Accepted by ESEC/FSE-2022

The release note is an essential software artifact of open-source software that documents crucial information about changes, such as new features and bug fixes. With the help of release notes, both developers and users could have a general understanding of the latest version without browsing the source code. However, it is a daunting and time-consuming job for developers to produce release notes. Although prior studies have provided some automatic approaches, they generate release notes mainly by extracting information from code changes. This will result in language-specific and not being general enough to be applicable. Therefore, helping developers produce release notes effectively remains an unsolved challenge. To address the problem, we first conduct a manual study on the release notes of 900 GitHub projects, which reveals that more than 54% of projects produce their release notes with pull requests. Based on the empirical finding, we propose a deep learning based approach named DeepRelease (Deep learning based Release notes generator) to generate release notes according to pull requests. The process of release notes generation in DeepRelease includes the change entries generation and the change category (i.e., new features or bug fixes) generation, which are formulated as a text summarization task and a multi-class classification problem, respectively. Since DeepRelease fully employs text information from pull requests to summarize changes and identify the change category, it is language-agnostic and can be used for projects in any language. We build a dataset with over 46K release notes and evaluate DeepRelease on the dataset. The experimental results indicate that DeepRelease outperforms four baselines and can generate release notes similar to those manually written ones in a fraction of the time., Comment: Accepted at APSEC'21