Helping Your Docker Images to Spread Based on Explainable Models

  • Riccardo GuidottiEmail author
  • Jacopo Soldani
  • Davide Neri
  • Antonio Brogi
  • Dino Pedreschi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11053)


Docker 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:


Docker images Popularity estimation Explainable models 



Work partially supported by the EU H2020 Program under the funding scheme “INFRAIA-1-2014-2015: Research Infrastructures”, grant agreement 654024 “SoBigData” (


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Riccardo Guidotti
    • 1
    • 2
    Email author
  • Jacopo Soldani
    • 1
  • Davide Neri
    • 1
  • Antonio Brogi
    • 1
  • Dino Pedreschi
    • 1
  1. 1.University of PisaPisaItaly
  2. 2.KDDLab, ISTI-CNRPisaItaly

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