People and Management Debt in ML-Integrated Software Projects: Structuring Industry Insights
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 techni...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11112555/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849236318599512064 |
|---|---|
| author | Pelin Dayan-Akman Ozden Ozcan-Top Tugba Taskaya AKMAN |
| author_facet | Pelin Dayan-Akman Ozden Ozcan-Top Tugba Taskaya AKMAN |
| author_sort | Pelin Dayan-Akman |
| collection | DOAJ |
| description | 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). 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 in an iterative incremental manner 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, 10 of which relate to people debt, and the remaining 5 to management debt. Each category is associated with underlying causes, short-term fixes, and potential solutions. Our findings show that NTD in ML projects frequently arise from inadequate decision-making practices, particularly those related to technology adoption, knowledge management, and human resource planning. Additional sources of NTD include challenges in team dynamics, such as insufficient collaboration, poor skill integration, and ineffective team structuring, as well as communication barriers rooted in organizational culture and team interactions. These factors collectively and substantially impact project outcomes. While band-aid solutions may provide short-term relief, they frequently contribute to accumulation over time. To support practitioners and researchers, our study complements the proposed artifact with actionable recommendations informed by expert perspectives and literature. |
| format | Article |
| id | doaj-art-76891e63ddb44ae28ebd8156f4600ac0 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-76891e63ddb44ae28ebd8156f4600ac02025-08-20T04:02:18ZengIEEEIEEE Access2169-35362025-01-011313701213703210.1109/ACCESS.2025.359560911112555People and Management Debt in ML-Integrated Software Projects: Structuring Industry InsightsPelin Dayan-Akman0https://orcid.org/0009-0003-1152-0802Ozden Ozcan-Top1https://orcid.org/0000-0001-6608-0726Tugba Taskaya AKMAN2https://orcid.org/0000-0001-7387-8621Graduate School of Informatics, Middle East Technical University, Ankara, TürkiyeGraduate School of Informatics, Middle East Technical University, Ankara, TürkiyeGraduate School of Informatics, Middle East Technical University, Ankara, TürkiyeThe 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). 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 in an iterative incremental manner 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, 10 of which relate to people debt, and the remaining 5 to management debt. Each category is associated with underlying causes, short-term fixes, and potential solutions. Our findings show that NTD in ML projects frequently arise from inadequate decision-making practices, particularly those related to technology adoption, knowledge management, and human resource planning. Additional sources of NTD include challenges in team dynamics, such as insufficient collaboration, poor skill integration, and ineffective team structuring, as well as communication barriers rooted in organizational culture and team interactions. These factors collectively and substantially impact project outcomes. While band-aid solutions may provide short-term relief, they frequently contribute to accumulation over time. To support practitioners and researchers, our study complements the proposed artifact with actionable recommendations informed by expert perspectives and literature.https://ieeexplore.ieee.org/document/11112555/Artificial intelligencemachine learningmachine learning life cycletechnical debtnontechnical debtpeople debt |
| spellingShingle | Pelin Dayan-Akman Ozden Ozcan-Top Tugba Taskaya AKMAN People and Management Debt in ML-Integrated Software Projects: Structuring Industry Insights IEEE Access Artificial intelligence machine learning machine learning life cycle technical debt nontechnical debt people debt |
| title | People and Management Debt in ML-Integrated Software Projects: Structuring Industry Insights |
| title_full | People and Management Debt in ML-Integrated Software Projects: Structuring Industry Insights |
| title_fullStr | People and Management Debt in ML-Integrated Software Projects: Structuring Industry Insights |
| title_full_unstemmed | People and Management Debt in ML-Integrated Software Projects: Structuring Industry Insights |
| title_short | People and Management Debt in ML-Integrated Software Projects: Structuring Industry Insights |
| title_sort | people and management debt in ml integrated software projects structuring industry insights |
| topic | Artificial intelligence machine learning machine learning life cycle technical debt nontechnical debt people debt |
| url | https://ieeexplore.ieee.org/document/11112555/ |
| work_keys_str_mv | AT pelindayanakman peopleandmanagementdebtinmlintegratedsoftwareprojectsstructuringindustryinsights AT ozdenozcantop peopleandmanagementdebtinmlintegratedsoftwareprojectsstructuringindustryinsights AT tugbataskayaakman peopleandmanagementdebtinmlintegratedsoftwareprojectsstructuringindustryinsights |