ReBack: recommending backports in social coding environments

Abstract

Pull-based development is widely used in popular social coding environments like GitHub and GitLab for both internal and external contributions. When critical bug fixes or features are committed to the main branch of a project, it is often desirable to also port those changes to other stable branches. This process is referred to as backporting, and pull-requests in the process are known as backports. Backports are typically determined after extensive discussion with collaborators, and it may take many days to identify backports, which commonly results in tags and references to the original pull-requests (i.e., pull-requests for the main branch) being missed. To help software development teams better identify and manage backports, we propose ReBack (Recommending Backports), a tool based on a deep-learning model for automatically identifying backports from pull-requests and related reviews, discussions, metadata, and committed code. ReBack predicted backports with 90.98% precision and 91.81% recall from 80,000 pull-requests in 17 GitHub projects. Although the results are promising, more research is required to further support backporting, including research into automatically porting a pull-request to further reduce costs when managing software versions and branches.