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Intertwining Ecosystems: A Large Scale Empirical Study of Libraries that Cross Software Ecosystems

Intertwining Ecosystems: A Large Scale Empirical Study of Libraries that Cross Software Ecosystems

Abstract

An increase in diverse technology stacks and third-party library usage has led developers to inevitably switch technologies. To assist these developers, maintainers have started to release their libraries to multiple technologies, i.e., a cross-ecosystem library. Our goal is to explore the extent to which these cross-ecosystem libraries are intertwined between ecosystems. We perform a large- scale empirical study of 1.1 million libraries from five different software ecosystems, i.e., PyPI for Python, CRAN for R, Maven for Java, RubyGems for Ruby, and NPM for JavaScript to identify 4,146 GitHub projects that release libraries to these five ecosystems. Analyzing their contributions, we first find that a significant majority (median of 37.5%) of contributors of these cross-ecosystem libraries come from a single ecosystem, while also receiving a significant portion of contributions (median of 24.06%) from outside their target ecosystems. We also find that a cross-ecosystem library is written using multiple programming languages. Specifically, three (i.e., PyPI, CRAN, RubyGems) out of the five ecosystems has the majority of source code is written using languages not specific to that ecosystem. As ecosystems become intertwined, this opens up new avenues for research, such as whether or not cross-ecosystem libraries will solve the search for replacement libraries, or how these libraries fit within each ecosystem just to name a few.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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