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On the Diversity of Software Package
Popularity Metrics: An Empirical
Study of npm
Ahmed Zerouali, Tom Mens, Gregorio Robles, Jesus Gonzalez
Barahona
The IEEE International Conference on Software Analysis, Evolution
and Reengineering (SANER)
Hangzhou, China - February 24-27, 2019
/Motivation
/Motivation
/Goal
How are metrics of package popularity related to each other?
/Method/ Dataset
175,774 packages
/Method/ 9 popularity metrics
# runtime dependent repositories (libraries.io)
# transitive runtime dependents (libraries.io)
# direct runtime dependents (npm and libraries.io)
# downloads (npm)
# npm stars (npm)
# github stars (GitHub)
# forks (GitHub)
# pull requests (GitHub)
# subscribers (GitHub)
/Metrics Emanating From the Same Source
/Metrics Emanating From the Same Source
/Metrics Emanating From the Same Source
/Metrics Emanating from Different Sources/ 6 metrics
# runtime dependent repositories (libraries.io)
# direct runtime dependents (npm and libraries.io)
# downloads (npm)
# npm stars (npm)
Aggarwal-Popularity = #forks + #stars + #pull requests² (GitHub)
# subscribers (GitHub)
/Metrics Emanating from Different Sources/ 6 metrics
/Metrics Emanating from Different Sources/ 6 metrics
/Limitations
- There are other metrics not considered in the study (e.g. development
dependents, SoureRank (from libraries.io), etc)
- We focused only on packages that are hosted on GitHub.
- We focused only on one package repository, npm.
/Conclusion
- Conducted analysis on the diversity of software popularity metrics.
- Popularity metrics are not strongly correlated, implying that the use of
different metrics may produce different outcomes.
- There is a need to define a measurement framework that takes into account
the diversity and context- dependence of software popularity.
/Future Work
- Do a more fine grained survey on software popularity metrics.
- Consider concrete examples of studies and reproduce them using other
popularity metrics.
- Extend the analysis on other ecosystems.
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On the Diversity of Software Package Popularity Metrics: An Empirical Study of npm

  • 1. On the Diversity of Software Package Popularity Metrics: An Empirical Study of npm Ahmed Zerouali, Tom Mens, Gregorio Robles, Jesus Gonzalez Barahona The IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) Hangzhou, China - February 24-27, 2019
  • 4. /Goal How are metrics of package popularity related to each other?
  • 6. /Method/ 9 popularity metrics # runtime dependent repositories (libraries.io) # transitive runtime dependents (libraries.io) # direct runtime dependents (npm and libraries.io) # downloads (npm) # npm stars (npm) # github stars (GitHub) # forks (GitHub) # pull requests (GitHub) # subscribers (GitHub)
  • 7. /Metrics Emanating From the Same Source
  • 8. /Metrics Emanating From the Same Source
  • 9. /Metrics Emanating From the Same Source
  • 10. /Metrics Emanating from Different Sources/ 6 metrics # runtime dependent repositories (libraries.io) # direct runtime dependents (npm and libraries.io) # downloads (npm) # npm stars (npm) Aggarwal-Popularity = #forks + #stars + #pull requests² (GitHub) # subscribers (GitHub)
  • 11. /Metrics Emanating from Different Sources/ 6 metrics
  • 12. /Metrics Emanating from Different Sources/ 6 metrics
  • 13. /Limitations - There are other metrics not considered in the study (e.g. development dependents, SoureRank (from libraries.io), etc) - We focused only on packages that are hosted on GitHub. - We focused only on one package repository, npm.
  • 14. /Conclusion - Conducted analysis on the diversity of software popularity metrics. - Popularity metrics are not strongly correlated, implying that the use of different metrics may produce different outcomes. - There is a need to define a measurement framework that takes into account the diversity and context- dependence of software popularity.
  • 15. /Future Work - Do a more fine grained survey on software popularity metrics. - Consider concrete examples of studies and reproduce them using other popularity metrics. - Extend the analysis on other ecosystems.