  Software systems often leverage on open source software libraries to reuse
functionalities. Such libraries are readily available through software package
managers like npm for JavaScript. Due to the huge amount of packages available
in such package distributions, developers often decide to rely on or contribute
to a software package based on its popularity. Moreover, it is a common
practice for researchers to depend on popularity metrics for data sampling and
choosing the right candidates for their studies. However, the meaning of
popularity is relative and can be defined and measured in a diversity of ways,
that might produce different outcomes even when considered for the same
studies. In this paper, we show evidence of how different is the meaning of
popularity in software engineering research. Moreover, we empirically analyse
the relationship between different software popularity measures. As a case
study, for a large dataset of 175k npm packages, we computed and extracted 9
different popularity metrics from three open source tracking systems:
libraries.io, npmjs.com and GitHub. We found that indeed popularity can be
measured with different unrelated metrics, each metric can be defined within a
specific context. This indicates a need for a generic framework that would use
a portfolio of popularity metrics drawing from different concepts.
