AI-Augmented Automation for DevOps, a Model-Based Framework for Continuous Development in Cyber-Physical Systems

Abstract

The main purpose of this paper is to review how AI-augmented Automation for DevOps is helping the modeling of Cyber-Physical Systems. The growing complexity in the creation and operation of Cyber-Physical Systems (CPS) necessitates a more effective engineering methodology. The main objective of the paper is to develop a better understanding of the model-based framework for more effectively supporting large and complex Cyber-Physical Systems (CPS) software and system engineering using AI augmentation. Recent years have seen a rise in the popularity of DevOps, a methodology that encourages developers and operations personnel to work together more closely on systems [1]. When it comes to system design and integration, using artificial intelligence (AI) is helpful, but it's still restricted due to its tremendous potential. AI technology is expected to play a significant role in the future automation of many major corporations [1]. While the number of organizations investing substantial money in software development is continually expanding, AI is still a developing technology. An integrated AI-augmented framework for the automatic continuous creation of CPSs is the goal of the project, which will use a model-based approach [1]. Model-Driven Engineering (MDE) ideas and methodologies will be covered thoroughly to give a model-based framework with appropriate approaches and associated technology

Keywords: Machine learning, clinical research, clinical trials, biotechnology, Biomedical innovation