Legacy Modernization with AI -- Mainframe modernization

TL;DR

This paper discusses how AI-driven strategies like automated code refactoring, data migration, and predictive maintenance can modernize legacy mainframe systems into flexible, scalable architectures, enhancing operational efficiency and enabling digital transformation.

Contribution

It introduces AI-assisted methods for mainframe modernization, including automated code analysis, migration tools, and predictive maintenance techniques, which are novel approaches in this domain.

Findings

AI enables automated code refactoring and testing.

Machine learning models identify efficiency improvements.

AI-driven strategies reduce downtime and improve resilience.

Abstract

Artificial Intelligence-assisted legacy modernization is essential in changing the stalwart mainframe systems of the past into flexible, scalable, and smart architecture. While mainframes are generally dependable, they can be difficult to maintain due to their high maintenance costs, the shortage of skills, and the problems in integrating them with cloud-based systems. By adopting AI-driven modernization strategies such as automated code refactoring, migration of data using smart tools, and predictive maintenance, companies can easily move to microservices, containerized environments, and hybrid cloud platforms. Machine learning models have the capability to go through legacy codebases, figure out efficiency opportunities, and carry out automated testing and deployment. Besides that, AI improves the organization's operational efficiency by generating the insights that can be used to level the workload and detect the anomalies. The coupling of the two is not only about saving the core business logic but also about enabling quicker innovation, less downtime, and enhanced system resilience. Therefore, the use of AI in mainframe modernization is a catalyst for digital transformation and enterprise growth that is sustainable over time.