Legacy application modernization entails updating or modernizing legacy applications. Typically hosted on mainframe computers or any other legacy systems that have been in use for several decades, these systems — once the backbone of computing in large organizations — have become outdated and difficult to maintain; with the advent of newer technologies, legacy apps pose challenges like operational inefficiencies, high costs and limited scalability.

Modernization involves updating the applications to modern technologies, like cloud computing, micro-services and containerization, to enhance IT operations, reduce maintenance costs and increase agility, enabling organizations to adapt to the evolving business requirements swiftly.

There are various approaches to legacy application modernization, including re-hosting, re-platforming and re-architecting.

Leveraging transformer neural networks based on NLP for migrating from one programming language to another can be an optimal solution to streamline this complex and challenging process. The algorithm's accuracy, speed, scalability and reduced costs make it an attractive option for modernizing legacy systems. This approach converts COBOL to Java by treating the code as natural language and leveraging the transformer network's attention mechanism to generate equivalent Java code.

To learn more about the intricacies of legacy application modernization with the help of AI, download our whitepaper now.