A machine learning approach to predict DevOps readiness and adaptation in a heterogeneous IT environment

IF 2.4 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Frontiers in Computer Science Pub Date : 2023-10-06 DOI:10.3389/fcomp.2023.1214722
Gopalakrishnan Sriraman, Shriram R.
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Abstract

Software 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 efficiency. 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.
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一种在异构IT环境中预测DevOps准备和适应的机器学习方法
在这个互联的世界里,软件和信息系统已经成为每个企业的核心竞争力。软件交付和操作的任何改进都将极大地影响企业和社会。可持续的软件开发是软件组织关注的关键领域之一。利用人工智能和云计算从软件中提供持续价值的智能自动化应用在整个行业处于初级阶段,并且正在迅速发展。随着DevOps的出现,敏捷方法提高了软件质量,加快了交付速度。许多软件组织已经采用DevOps来开发和操作他们的软件系统并提高效率。软件组织试图通过利用各种专家服务来实现DevOps活动。软件组织对DevOps的采用面临着多重挑战。这些问题可以通过理解和结构化地解决痛点来克服。本文对相关利益相关者的访谈进行了初步分析。我们建立了基础真理,并将其应用于评估各种机器学习算法,以比较它们的准确性并测试我们的假设。本研究旨在帮助研究人员和实践者理解DevOps的采用以及DevOps实践可行的环境。实验结果将表明,机器学习可以预测组织采用DevOps的准备情况。
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来源期刊
Frontiers in Computer Science
Frontiers in Computer Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.30
自引率
0.00%
发文量
152
审稿时长
13 weeks
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