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.

论文《人工智能赋能系统中的代码与架构债务：论相关性、严重性、影响及缓解策略》的复现包。
本研究依托行业从业者的专业实践经验，为学术共同体提供具有价值的洞见，旨在提升研究者对人工智能技术债务（AI technical debt）检测与缓解工作的关注度。本研究的最终目标是通过提供工具与方法论，赋能行业从业者；此外，我们的研究还揭示了从业者可能尚未完全熟悉的全新维度，助力学界对该主题形成更深入的认知。
我们发起了一项包含53名AI开发者的调研，采集了两类核心人工智能技术债务——架构债务与代码债务——的实际相关性、严重程度及影响相关数据，同时也收集了从业者用于识别与缓解此类债务的具体策略。本调研的核心结论显示，代码债务与架构债务会对人工智能赋能系统的质量产生多重负面影响，且当前从业者可获取的应对支持极为有限。
本文最后提炼了本次研究的经验总结与可供研究者参考的可落地实践启示，作为全文收尾。