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ReBack: recommending backports in social coding environments

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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.

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Acknowledgements

This research is supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery grants, and by an NSERC Collaborative Research and Training Experience (CREATE) grant, and by two Canada First Research Excellence Fund (CFREF) grants coordinated by the Global Institute for Food Security (GIFS) and the Global Institute for Water Security (GIWS).

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  1. Department of Computer Science, University of Saskatchewan, 110 Science Place, Saskatoon, SK, S7N 5C9, Canada

    Debasish Chakroborti, Kevin A. Schneider & Chanchal K. Roy

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  1. Debasish Chakroborti
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All authors contributed to the study's conception and design. Material preparation, data collection, and analysis were performed by Debasish Chakroborti. The first draft of the manuscript was written by Debasish Chakroborti, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Chakroborti, D., Schneider, K.A. & Roy, C.K. ReBack: recommending backports in social coding environments. Autom Softw Eng 31, 18 (2024). https://doi.org/10.1007/s10515-024-00416-1

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