# On the Diversity of Software Package Popularity Metrics: An Empirical Study of npm

## TL;DR

This study investigates the diverse ways to measure software package popularity on npm, revealing that different metrics often capture unrelated aspects and emphasizing the need for a unified framework for measuring popularity.

## Contribution

It provides an empirical analysis of nine different popularity metrics across three sources, highlighting their differences and the necessity for a comprehensive measurement framework.

## Key findings

- Popularity metrics are often unrelated and context-specific.
- Different sources produce diverse popularity measures.
- A large dataset of 175k npm packages was analyzed.

## Abstract

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.