Replication Package of "Code and Architectural Debt in Artificial Intelligence-Enabled Systems: On theRelevance, Severity, Impact, and Mitigation Strategies"

Replication Package of the article "Code and Architectural Debt in Artificial Intelligence-Enabled Systems: On the<br>Relevance, Severity, Impact, and Mitigation Strategies".<br>In this paper, we leverage the expertise of practitioners to offer valuable insights to the research community, aiming to enhance awareness among researchers about the detection and mitigation of AI technical debt. Our ultimate goal is to empower practitioners by providing them with tools and methodologies. Additionally, our study sheds light on novel aspects that practitioners might not be fully acquainted with, contributing to a deeper understanding of the subject.We develop a survey study featuring 53 AI developers, in which we collect information on the practical relevance, severity, and impact of two key types of AI technical debt, such as architectural and code debt, other than the strategies applied by practitioners to identify and mitigate them.The key findings of the study reveal the multiple impacts that code and architectural debt may have on the quality of AI-enabled systems and the little support practitioners have to deal with them.We conclude the article by distilling lessons learned and actionable insights for researchers.