{"title":"Navigating the DevOps landscape","authors":"Xinrui Zhang , Pincan Zhao , Jason Jaskolka","doi":"10.1016/j.jss.2024.112331","DOIUrl":null,"url":null,"abstract":"<div><h3>Context:</h3><div>DevOps, with its increasing prevalence in both industry and academia, has evolved into various DevOps variants (namely XOps) to address emerging technological and operational challenges. However, this proliferation has created confusion and a lack of clarity about the systematic understanding of these XOps and their interrelationship in the DevOps landscape, leading to fragmented knowledge and application.</div></div><div><h3>Objective:</h3><div>This research seeks to construct a comprehensive picture of the existing DevOps landscape, clarifying the nature and nuances of various XOps, to guide effective future studies and implementations.</div></div><div><h3>Method:</h3><div>Utilizing Multivocal Literature Review (MLR), 80 gathered documents are thoroughly examined from throughout the whole community, encompassing both white and grey literature, to map the DevOps landscape.</div></div><div><h3>Results:</h3><div>Our review systematically discovered 38 XOps terms and 13 well-studied XOps including AIOps, BizDevOps, CloudOps, DataOps, DevSecOps, FinOps, GitOps, MLOps, ModelOps, NetDevOps, NoOps, SecDevOps and TwinOps. We provided dictionary-like resource that elucidates the core concepts and main ideas associated with each XOps. An in-depth understanding of intricate evolution from DevOps to XOps is delved into, supplemented by the research of relationships between XOps and various technological enablers as well as relationships between XOps and organizational teams, contributing to the ongoing dialogue surrounding their application and evolution.</div></div><div><h3>Implications:</h3><div>This paper provides a foundational understanding of the DevOps landscape including open issues and challenges, current and future trends, assisting both researchers and practitioners in navigating this complex field. It establishes a platform for further research and practical applications in the evolving field of DevOps and XOps.</div></div>","PeriodicalId":51099,"journal":{"name":"Journal of Systems and Software","volume":"223 ","pages":"Article 112331"},"PeriodicalIF":3.7000,"publicationDate":"2025-01-14","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/S0164121224003753","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}
引用次数: 0
Abstract
Context:
DevOps, with its increasing prevalence in both industry and academia, has evolved into various DevOps variants (namely XOps) to address emerging technological and operational challenges. However, this proliferation has created confusion and a lack of clarity about the systematic understanding of these XOps and their interrelationship in the DevOps landscape, leading to fragmented knowledge and application.
Objective:
This research seeks to construct a comprehensive picture of the existing DevOps landscape, clarifying the nature and nuances of various XOps, to guide effective future studies and implementations.
Method:
Utilizing Multivocal Literature Review (MLR), 80 gathered documents are thoroughly examined from throughout the whole community, encompassing both white and grey literature, to map the DevOps landscape.
Results:
Our review systematically discovered 38 XOps terms and 13 well-studied XOps including AIOps, BizDevOps, CloudOps, DataOps, DevSecOps, FinOps, GitOps, MLOps, ModelOps, NetDevOps, NoOps, SecDevOps and TwinOps. We provided dictionary-like resource that elucidates the core concepts and main ideas associated with each XOps. An in-depth understanding of intricate evolution from DevOps to XOps is delved into, supplemented by the research of relationships between XOps and various technological enablers as well as relationships between XOps and organizational teams, contributing to the ongoing dialogue surrounding their application and evolution.
Implications:
This paper provides a foundational understanding of the DevOps landscape including open issues and challenges, current and future trends, assisting both researchers and practitioners in navigating this complex field. It establishes a platform for further research and practical applications in the evolving field of DevOps and XOps.
背景:随着DevOps在工业界和学术界的日益流行,它已经演变成各种DevOps变体(即XOps),以应对新兴的技术和操作挑战。然而,这种扩散造成了对这些XOps及其在DevOps领域中的相互关系的系统理解的混乱和缺乏清晰度,导致了支离破碎的知识和应用。目的:本研究旨在构建现有DevOps全景图,澄清各种XOps的性质和细微差别,以指导有效的未来研究和实施。方法:利用Multivocal Literature Review (MLR),从整个社区收集80个文档,包括白色和灰色文献,以绘制DevOps景观。结果:我们系统地发现了38个XOps术语和13个经过充分研究的XOps,包括AIOps、BizDevOps、CloudOps、DataOps、DevSecOps、FinOps、GitOps、MLOps、ModelOps、NetDevOps、NoOps、SecDevOps和TwinOps。我们提供了类似词典的资源,阐明了与每个xop相关的核心概念和主要思想。本书深入研究了从DevOps到XOps的复杂演变,并辅以对XOps与各种技术推动者之间关系的研究,以及XOps与组织团队之间关系的研究,为围绕其应用和演变的持续对话做出了贡献。含义:本文提供了对DevOps前景的基本理解,包括开放的问题和挑战,当前和未来的趋势,帮助研究人员和实践者驾驭这个复杂的领域。它为发展中的DevOps和XOps领域的进一步研究和实际应用建立了一个平台。
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.