A machine learning approach to predict DevOps readiness and adaptation in a heterogeneous IT environment - Sriraman, Gopalakrishnan; R., Shriram (Frontiers in Computer Science, 2023)
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 of software organisations. Application of intelligent Automation leveraging Artificial Intelligence and Cloud Computing in delivering the continuous value of software is in the nascent stage across the industry and evolving rapidly. The arrival of agile methodologies with DevOps has increased software quality and accelerated its delivery. Numerous software organisations have adopted DevOps to develop and operate their software systems and improve efficiency. Software organisations try to implement DevOps activities by taking advantage of various expert services. The adoption of DevOps by software organisations is beset with multiple issues. These issues can be overcome by understanding and addressing the pain points structurally. This paper will present the interviews' preliminary analysis to the relevant stakeholders. Ground Truths were established and applied to evaluate various machine learning algorithms to compare the accuracy and test our hypothesis. This study aims to help researchers and practitioners understand the DevOps adoptions and the contexts in which the practices will be viable. Experimental results will show that machine learning can predict an organisation's readiness to adopt DevOps.