[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123665-en":3,"doc-seo-123665-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123665,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","A machine learning approach to predict DevOps readiness and adaptation in a heterogeneous IT environment - read online free","A machine learning framework is used to predict an organization’s readiness to adopt DevOps and to assess adaptation in a heterogeneous IT environment. The study conducts a preliminary analysis of interviews with relevant stakeholders, establishes ground truths, and applies multiple machine learning algorithms to compare accuracy and test a hypothesis. Results indicate that machine learning can forecast adoption readiness, helping researchers and practitioners understand when DevOps practices are viable and what contextual conditions enable effective implementation.","TYPE Original Research PUBLISHED 06 October 2023  \nDOI 10. 3389/fcomp.2023.1214722  \nOPEN ACCESS  \nEDITED BY  \nNiusha Shafiabady,  \nCharles Darwin University, Australia  \nREVIEWED BY  \nHerman Wandabwa,  \nAuckland University of Technology, New Zealand  \nMirna Muñoz,  \nCentro de Investigación en Matemáticas, Mexico  \n*CORRESPONDENCE  \nGopalakrishnan Sriraman  \n [gopalakrishnan.sriraman2019@vitbhopal.ac.in](gopalakrishnan.sriraman2019@vitbhopal.ac.in)  \nRECEIVED 30 April 2023  \nACCEPTED 13 September 2023  \nPUBLISHED 06 October 2023  \nCITATION  \nSriraman G and R. S (2023) A machine learning approach to predict DevOps readiness and adaptation in a heterogeneous IT environment. Front. Comput. Sci. 5:1214722 .  \ndoi: 10.3389/fcomp.2023.1214722  \nCOPYRIGHT  \n© 2023 Sriraman and R. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nA machine learning approach to predict DevOps readiness and adaptation in a heterogeneous IT environment  \nGopalakrishnan Sriraman* and Shriram R.  \nDepartment of Computing Science and Engineering, VIT Bhopal University, Sehore, MP, India  \nSoftware and information systems have become a core competency for every business in this connected world. Any enhancement in software delivery and operations will tremendously impact businesses and society. Sustainable software development is one of the key focus areas for software organizations. The application of intelligent automation leveraging artificial intelligence and cloud computing to deliver continuous value from software is in its nascent stage across the industry and is evolving rapidly. The advent of agile methodologies with DevOps has increased software quality and accelerated its delivery. Numerous software organizations have adopted DevOps to develop and operate their software systems and improve e􀀈ciency. Software organizations try to implement DevOps activities by taking advantage of various expert services. The adoption of DevOps by software organizations is beset with multiple challenges. These issues can be overcome by understanding and structurally addressing the pain points. This paper presents the preliminary analysis of the interviews with the relevant stakeholders. Ground truths were established and applied to evaluate various machine learning algorithms to compare their accuracy and test our hypothesis. This study aims to help researchers and practitioners understand the adoption of DevOps and the contexts in which the DevOps practices are viable. The experimental results will show that machine learning can predict an organization’s readiness to adopt DevOps.  \nKEYWORDS  \nDevOps, machine learning, survey, adaption, accelerated software delivery, continuous delivery pipeline, technical agility  \n1. Introduction  \nDevOps helps businesses accelerate software delivery and experience to deliver optimal value. More broadly, DevOps is a philosophy that promotes better communication and collaboration between these teams. In this context, other observed trends include software increasingly being delivered over the Internet, either server-side (for example, Software-as-a-Service) or as a channel with direct delivery to the customer. Mobile platforms and their technology becoming increasingly pervasive and technologies upon which this software runs (Varia and Mathew, 2014) .  \nAgile and DevOps process models require the delivery and execution of software engineering activities to add real value to the business. A fundamental success factor for the Agile and DevOps process models is the continuous delivery of incremental value to the organization. This requires an","cbCaioimPi62qisj","https://ap.wps.com/l/cbCaioimPi62qisj","pdf",495469,1,10,"English","en",105,"# Introduction\n## DevOps background and motivation\n## Key practices and continuous delivery\n## Related trends and challenges","[{\"question\":\"What is the document’s main research goal?\",\"answer\":\"To use machine learning to predict an organization’s readiness to adopt DevOps and to evaluate adaptation in a heterogeneous IT environment.\"},{\"question\":\"How are ground truths obtained and used?\",\"answer\":\"Ground truths are established from preliminary stakeholder interview analysis, then used to evaluate different machine learning algorithms and test the study’s hypothesis.\"},{\"question\":\"Which DevOps elements does the paper reference as essential practices?\",\"answer\":\"It highlights continuous integration, continuous delivery, infrastructure as code, and monitoring and logging as core DevOps practices.\"}]","A machine learning approach to predict DevOps readiness and adaptation in a heterogeneous IT environment - 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