When AI Joins the Team! A Model of How AI Adoption Relates To Social Patterns in Software Engineering Teams

2026-08-04

AI Summary

This study investigates the impact of AI tool integration on community smells—socio-technical anti-patterns reflecting breakdowns in collaboration and communication—within software engineering teams. Grounded in Transactive Memory System (TMS) theory, the research develops and empirically validates an interaction model capturing human–AI and human–human dynamics along dimensions of specialization and coordination. Drawing on survey data from 152 software professionals and employing PLS-SEM, factor analysis, and expert validation, the work introduces the first TMS-driven, reusable measurement framework for community smells. Findings reveal that in specialization tasks, AI fosters knowledge-sharing peer interactions and mitigates community smells, whereas in coordination tasks, AI enhances communication quality by complementing rather than replacing interpersonal collaboration, with effects varying across collaboration types.

Abstract

Context: The growing adoption of AI-assisted development tools is changing how software teams collaborate, share knowledge, and coordinate, yet its consequences for team social dynamics remain largely unexplored. Gap: It is unclear whether AI adoption is associated with an increase or reduction in community smells,socio-technical anti-patterns reflecting coordination and communication breakdowns,and through which mechanisms. Method: Grounded in Transactive Memory Systems (TMS) theory, we validate instruments for HumanAI and HumanHuman interaction along two TMS dimensions, Specialization and Coordination, and test five PLS-SEM models on survey data from 152 software professionals using AI tools. Community smell constructs were derived from the literature and validated through expert surveys and factor analysis. Results: AI adoption relates to community smells not in a single way, but through mechanisms depending on the work. In specialization work, AI is associated with higher knowledge-sharing peer interaction, which is in turn associated with fewer smells. In coordination work, AI is directly associated with higher communication quality, complementing rather than replacing human interaction. Contributions: We provide an empirically validated, TMS-grounded model showing that the AIcommunity-smell relationship is contingent on the type of collaboration, with a reusable instrument and evidence-based implications for research and practice.

Problem

• AI adoption
• community smells
• software engineering teams
• social dynamics
• socio-technical anti-patterns

Innovation

• Transactive Memory Systems
• AI adoption
• community smells
• PLS-SEM
• human-AI collaboration