How Do Communities of ML-Enabled Systems Smell? A Cross-Sectional Study on the Prevalence of Community Smells

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.