01318nas a2200157   4500008004100000245006900041210006900110260001300179520080200192100002300994700002001017700001701037700001901054700002001073856006701093       2018                        eng d00aHelping your docker images to spread based on explainable models0 aHelping your docker images to spread based on explainable models  bSpringer3 aDocker is on the rise in today’s enterprise IT. It permits shipping applications inside portable containers, which run from so-called Docker images. Docker images are distributed in public registries, which also monitor their popularity. The popularity of an image impacts on its actual usage, and hence on the potential revenues for its developers. In this paper, we present a solution based on interpretable decision tree and regression trees for estimating the popularity of a given Docker image, and for understanding how to improve an image to increase its popularity. The results presented in this work can provide valuable insights to Docker developers, helping them in spreading their images. Code related to this paper is available at: https://github.com/di-unipi-socc/DockerImageMiner.1 aGuidotti, Riccardo1 aSoldani, Jacopo1 aNeri, Davide1 aBrogi, Antonio1 aPedreschi, Dino  uhttps://link.springer.com/chapter/10.1007/978-3-030-10997-4_13