The Challenges and Mitigation Strategies of Using DevOps during Software Development

Abstract

This paper looked at the challenges that DevOpsbrings to software development, as well as the techniquesfor mitigating such challenges. Every industry, from financeto retail manufacturing, relies on software. Individualdesktop programs, large-scale web applications, and mobileapps are allexamples of software [1]. Due to software'sintangible and complicated nature, requirements change fast, and software development teams have found it difficultto generate software that adequately meets clientexpectations while also providing the desired functionalityand software quality [1]. Innovations have advanced thesoftware business fast. Researchers have developed newprogramming languages, new database designs, and newdevelopment technologies such asNCloud Computing,Crowdsourcing, APIs, and SOA as a burgeoning businessthat is just 50 to 60 years old [2]. Because of the introduction of new technologies, old software systemsbecome out-of-date and must be improved for the companyto remain viable. Because software is always evolving,several Software Development Life Cycle (SDLC) modelshave emerged, including the Waterfall approach, Iterative Model, Spiral Model, Agile, and derivative forms [2]. Thesemodels all cater to software development, deployment, and maintenance. With today's technological advancements, theindustry is moving toward the "DevOps" paradigm ofsoftware development. To provide software and services quickly, reliably, and of better quality, DevOps uses avariety of methodologies that bring together developers andoperations personnel. In a team empowered with completeresponsibility for their service and its underlying technological stack, duties and responsibilities are sharedfrom development through deployment and maintenance[3]. This study is meant to verify and assess whether theproblems and mitigation measures of implementing DevOps from Systematic literature research are widespread inbusiness

Keywords: DevOps, CI, CD, machine learning, CI/CD Pipeline, software development