How Do Community Smells Influence Self-Admitted Technical Debt in Machine Learning Projects?

TL;DR

This study investigates how community organizational issues influence the accumulation of technical debt in open-source machine learning projects, revealing widespread community smells and their correlation with technical debt across project sizes.

Contribution

It provides the first comprehensive analysis of community smells and their relationship with self-admitted technical debt in ML projects, highlighting size-dependent trends and key smell types.

Findings

Community smells are widespread in ML projects.

Certain smells correlate strongly with higher technical debt.

Project size influences the prevalence and evolution of community smells and debt.

Abstract

Community smells reflect poor organizational practices that often lead to socio-technical issues and the accumulation of Self-Admitted Technical Debt (SATD). While prior studies have explored these problems in general software systems, their interplay in machine learning (ML)-based projects remains largely underexamined. In this study, we investigated the prevalence of community smells and their relationship with SATD in open-source ML projects, analyzing data at the release level. First, we examined the prevalence of ten community smell types across the releases of 155 ML-based systems and found that community smells are widespread, exhibiting distinct distribution patterns across small, medium, and large projects. Second, we detected SATD at the release level and applied statistical analysis to examine its correlation with community smells. Our results showed that certain smells, such as Radio Silence and Organizational Silos, are strongly correlated with higher SATD occurrences. Third, we considered the six identified types of SATD to determine which community smells are most associated with each debt category. Our analysis revealed authority- and communication-related smells often co-occur with persistent code and design debt. Finally, we analyzed how the community smells and SATD evolve over the releases, uncovering project size-dependent trends and shared trajectories. Our findings emphasize the importance of early detection and mitigation of socio-technical issues to maintain the long-term quality and sustainability of ML-based systems.