Effective software development relies on managing both collaboration and
technology, but sociotechnical challenges can harm team dynamics and increase
technical debt. Although teams working on ML enabled systems are
interdisciplinary, research has largely focused on technical issues, leaving
their socio-technical dynamics underexplored. This study aims to address this
gap by examining the prevalence, evolution, and interrelations of community
smells, in open-source ML projects. We conducted an empirical study on 188
repositories from the NICHE dataset using the CADOCS tool to identify and
analyze community smells. Our analysis focused on their prevalence,
interrelations, and temporal variations. We found that certain smells, such as
Prima Donna Effects and Sharing Villainy, are more prevalent and fluctuate over
time compared to others like Radio Silence or Organizational Skirmish. These
insights might provide valuable support for ML project managers in addressing
socio-technical issues and improving team coordination.

Dieser Artikel untersucht Zeitreisen und deren Auswirkungen.

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