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

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.