  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.
