从DevOps到生成式AI的技术债务的演变:多声音文献综述

IF 4.1 2区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Sergio Moreschini , Elvira-Maria Arvanitou , Elisavet-Persefoni Kanidou , Nikolaos Nikolaidis , Ruoyu Su , Apostolos Ampatzoglou , Alexander Chatzigeorgiou , Valentina Lenarduzzi
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引用次数: 0

摘要

背景:人工智能(AI)——包括机器学习(ML)和生成式人工智能——与软件系统的快速集成正在重塑软件开发生命周期。随着人工智能驱动的系统变得更加动态和复杂,传统的技术债务(TD)管理方法面临越来越大的局限性。同时,人工智能辅助开发引入了新的TD形式,特别是在可维护性、可解释性和数据治理方面。目的:本研究旨在探讨技术债务管理(TDM)必须如何适应人工智能增强软件开发的背景。它调查了(1)人工智能驱动系统中TD的演变,以及(2)在软件工程过程中使用人工智能技术的含义。方法:我们进行了一项多语种的文献综述,结合了同行评审研究和行业来源的见解。遵循既定的指导方针,我们系统地分析了61个主要来源,分类了TD类型和管理活动,并确定了人工智能时代出现的关键挑战和实践。结果:我们的研究结果表明,数据相关的、基础设施相关的和管道相关的TD在ML系统中特别普遍。机器学习操作(MLOps)实践越来越被认为是管理此类债务的关键,特别是在动态数据依赖和模型再培训方面。与此同时,人工智能生成的工件和自动化管道引入了新的治理和可维护性挑战。结论:人工智能系统中的技术债务需要持续的、自动化的、跨职能的管理策略。随着软件对数据和使用的响应不断发展,新的操作范式——以mlop和小语言模型操作(SLMOps)等实践为基础——将对确保软件的长期可持续性至关重要。本研究为研究人员和从业者导航人工智能和TD管理的交叉点提供了基础地图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
Journal of Systems and Software
Journal of Systems and Software 工程技术-计算机:理论方法
CiteScore
8.60
自引率
5.70%
发文量
193
审稿时长
16 weeks
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.
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