"People and Management Related Debt in ML-Integrated Software Development Projects: Structuring Insights from Industry"

"The accelerated development of Machine Learning (ML) tools, combined with broader access to frameworks and infrastructures, has driven the rapid adoption of ML-based solutions in industry. However, their integration into software systems introduces unique challenges, particularly for managing technical debt (TD). While existing frameworks\/standards such as Cross-Industry Standard Process for Data Mining (CRISP-DM) and ISO\/IEC 5338 provide guidance for ML development, they fail to address the complex interplay of technical and nontechnical factors contributing to TD. Traditional TD research focuses primarily on technical issues, but in ML systems, people, and management factors, referred to as nontechnical debt (NTD), play a critical role in TD accumulation and persistence. In this study, we investigate the underexplored dimension of NTD in ML-integrated software systems, focusing on people and management-related factors. Using Design Science Research (DSR) methodology, we developed an artifact that categorizes NTD issues in ML systems. As part of this process, we conducted semi-structured interviews with 18 professionals from 15 companies, examining 22 ML projects. Through thematic analysis, we identified 15 NTD categories, each associated with underlying causes, short-term fixes, and potential solutions. Our findings indicate that NTD in ML projects often arise from decision-making practices, team dynamics, and communication barriers, all of which substantially affect project outcomes. While temporary fixes (i.e., band-aid solutions) may provide short-term relief, they frequently contribute to the accumulation over time. To support practitioners and researchers, our study complements the proposed artifact with actionable recommendations informed by expert perspectives and literature."

机器学习（Machine Learning，ML）工具的加速发展，配合各类框架与基础设施的可及性提升，推动了基于ML的解决方案在工业界的快速普及与应用。然而，将其集成至软件系统的过程中会催生独特的挑战，尤以技术债务（technical debt，TD）的管理问题为甚。现有诸如跨行业数据挖掘标准流程（Cross-Industry Standard Process for Data Mining，CRISP-DM）与ISO/IEC 5338等框架与标准，虽为ML开发提供了规范化指导，却未能厘清导致技术债务产生的技术与非技术因素间的复杂交互关系。传统技术债务研究主要聚焦于技术层面的问题，但在ML系统中，人员与管理相关因素——即非技术债务（nontechnical debt，NTD）——在技术债务的积累与持续存续过程中扮演着关键角色。本研究针对ML集成软件系统中尚未得到充分探索的非技术债务维度展开调研，重点关注人员与管理相关的影响因素。本研究采用设计科学研究（Design Science Research，DSR）方法论，开发了一款可对ML系统中的非技术债务问题进行分类的设计制品。在此研究过程中，我们对来自15家企业的18名专业人员开展了半结构化访谈，对22个ML项目进行了深入分析。通过主题分析法，我们共识别出15类非技术债务，每一类均对应其潜在成因、短期补救措施与潜在解决方案。研究结果显示，ML项目中的非技术债务通常源于决策实践、团队协作动态与沟通壁垒，上述因素均会对项目成果产生显著负面影响。尽管临时补救措施（即权宜之计）可在短期内缓解问题，但往往会随着时间推移加剧债务的积累。为助力从业者与研究者开展相关工作，本研究结合专家观点与现有文献，为所提出的设计制品补充了一系列可落地的实践建议。