{"title":"从DevOps到生成式AI的技术债务的演变:多声音文献综述","authors":"Sergio Moreschini , Elvira-Maria Arvanitou , Elisavet-Persefoni Kanidou , Nikolaos Nikolaidis , Ruoyu Su , Apostolos Ampatzoglou , Alexander Chatzigeorgiou , Valentina Lenarduzzi","doi":"10.1016/j.jss.2025.112599","DOIUrl":null,"url":null,"abstract":"<div><h3>Background:</h3><div>The rapid integration of Artificial Intelligence (AI) – including Machine Learning (ML) and Generative AI – into software systems is reshaping the software development lifecycle. As AI-driven systems become more dynamic and complex, traditional approaches to Technical Debt (TD) management face increasing limitations. Simultaneously, AI-assisted development introduces new forms of TD, particularly in relation to maintainability, explainability, and data governance.</div></div><div><h3>Objective:</h3><div>This study aims to explore how Technical Debt Management (TDM) must adapt in the context of AI-enhanced software development. It investigates (1) the evolution of TD in AI-driven systems, and (2) the implications of using AI technologies within the software engineering process.</div></div><div><h3>Methods:</h3><div>We conducted a multivocal literature review, combining insights from both peer-reviewed research and industry sources. Following established guidelines, we systematically analyzed 61 primary sources, categorized TD types and management activities, and identified key challenges and practices emerging in the AI era.</div></div><div><h3>Results:</h3><div>Our findings reveal that data-related, infrastructure, and pipeline-related TD are particularly prevalent in ML systems. Machine Learning Operations (MLOps) practices are increasingly recognized as essential for managing such debt, especially in relation to dynamic data dependencies and model retraining. In parallel, AI-generated artifacts and automated pipelines introduce new governance and maintainability challenges.</div></div><div><h3>Conclusion:</h3><div>Technical Debt in AI systems demands continuous, automated, and cross-functional management strategies. As software evolves in response to data and usage, new operational paradigms – grounded in practices like MLOps and Small Language Model Operations (SLMOps) – will be vital to ensure long-term software sustainability. This study provides a foundational map for researchers and practitioners navigating the intersection of AI and TD management.</div></div>","PeriodicalId":51099,"journal":{"name":"Journal of Systems and Software","volume":"231 ","pages":"Article 112599"},"PeriodicalIF":4.1000,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Evolution of Technical Debt from DevOps to Generative AI: A multivocal literature review\",\"authors\":\"Sergio Moreschini , Elvira-Maria Arvanitou , Elisavet-Persefoni Kanidou , Nikolaos Nikolaidis , Ruoyu Su , Apostolos Ampatzoglou , Alexander Chatzigeorgiou , Valentina Lenarduzzi\",\"doi\":\"10.1016/j.jss.2025.112599\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background:</h3><div>The rapid integration of Artificial Intelligence (AI) – including Machine Learning (ML) and Generative AI – into software systems is reshaping the software development lifecycle. As AI-driven systems become more dynamic and complex, traditional approaches to Technical Debt (TD) management face increasing limitations. Simultaneously, AI-assisted development introduces new forms of TD, particularly in relation to maintainability, explainability, and data governance.</div></div><div><h3>Objective:</h3><div>This study aims to explore how Technical Debt Management (TDM) must adapt in the context of AI-enhanced software development. It investigates (1) the evolution of TD in AI-driven systems, and (2) the implications of using AI technologies within the software engineering process.</div></div><div><h3>Methods:</h3><div>We conducted a multivocal literature review, combining insights from both peer-reviewed research and industry sources. Following established guidelines, we systematically analyzed 61 primary sources, categorized TD types and management activities, and identified key challenges and practices emerging in the AI era.</div></div><div><h3>Results:</h3><div>Our findings reveal that data-related, infrastructure, and pipeline-related TD are particularly prevalent in ML systems. Machine Learning Operations (MLOps) practices are increasingly recognized as essential for managing such debt, especially in relation to dynamic data dependencies and model retraining. In parallel, AI-generated artifacts and automated pipelines introduce new governance and maintainability challenges.</div></div><div><h3>Conclusion:</h3><div>Technical Debt in AI systems demands continuous, automated, and cross-functional management strategies. As software evolves in response to data and usage, new operational paradigms – grounded in practices like MLOps and Small Language Model Operations (SLMOps) – will be vital to ensure long-term software sustainability. This study provides a foundational map for researchers and practitioners navigating the intersection of AI and TD management.</div></div>\",\"PeriodicalId\":51099,\"journal\":{\"name\":\"Journal of Systems and Software\",\"volume\":\"231 \",\"pages\":\"Article 112599\"},\"PeriodicalIF\":4.1000,\"publicationDate\":\"2025-08-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Systems and Software\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0164121225002687\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Systems and Software","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0164121225002687","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
The Evolution of Technical Debt from DevOps to Generative AI: A multivocal literature review
Background:
The rapid integration of Artificial Intelligence (AI) – including Machine Learning (ML) and Generative AI – into software systems is reshaping the software development lifecycle. As AI-driven systems become more dynamic and complex, traditional approaches to Technical Debt (TD) management face increasing limitations. Simultaneously, AI-assisted development introduces new forms of TD, particularly in relation to maintainability, explainability, and data governance.
Objective:
This study aims to explore how Technical Debt Management (TDM) must adapt in the context of AI-enhanced software development. It investigates (1) the evolution of TD in AI-driven systems, and (2) the implications of using AI technologies within the software engineering process.
Methods:
We conducted a multivocal literature review, combining insights from both peer-reviewed research and industry sources. Following established guidelines, we systematically analyzed 61 primary sources, categorized TD types and management activities, and identified key challenges and practices emerging in the AI era.
Results:
Our findings reveal that data-related, infrastructure, and pipeline-related TD are particularly prevalent in ML systems. Machine Learning Operations (MLOps) practices are increasingly recognized as essential for managing such debt, especially in relation to dynamic data dependencies and model retraining. In parallel, AI-generated artifacts and automated pipelines introduce new governance and maintainability challenges.
Conclusion:
Technical Debt in AI systems demands continuous, automated, and cross-functional management strategies. As software evolves in response to data and usage, new operational paradigms – grounded in practices like MLOps and Small Language Model Operations (SLMOps) – will be vital to ensure long-term software sustainability. This study provides a foundational map for researchers and practitioners navigating the intersection of AI and TD management.
期刊介绍:
The Journal of Systems and Software publishes papers covering all aspects of software engineering and related hardware-software-systems issues. All articles should include a validation of the idea presented, e.g. through case studies, experiments, or systematic comparisons with other approaches already in practice. Topics of interest include, but are not limited to:
•Methods and tools for, and empirical studies on, software requirements, design, architecture, verification and validation, maintenance and evolution
•Agile, model-driven, service-oriented, open source and global software development
•Approaches for mobile, multiprocessing, real-time, distributed, cloud-based, dependable and virtualized systems
•Human factors and management concerns of software development
•Data management and big data issues of software systems
•Metrics and evaluation, data mining of software development resources
•Business and economic aspects of software development processes
The journal welcomes state-of-the-art surveys and reports of practical experience for all of these topics.