IBM Introduces Watsonx Generative AI Code Assistant for Cobol Mainframe Modernization

The new tool helps to expedite the COBOL app modernization process while minimizing potential issues.

Cobol Mainframe Computer wiki commons

IBM has unveiled Watsonx Code Assistant for Z, aimed at aiding enterprises in the modernization of their crucial and often aged mainframe applications. The -powered coding assistant is designed to facilitate developers in translating applications written in the COBOL language to Java. COBOL, a language that debuted in 1959, continues to run many production applications today, while developers that are proficient in COBOL are hard to find.

IBM aims to provide businesses with a robust tool to transition from legacy COBOL systems to more modern and maintainable Java applications. The primary goal is to expedite the COBOL app modernization process while minimizing potential issues. IBM has announced that the technology will be showcased for the first time at the IBM TechXchange event in Las Vegas next month.

Watsonx Code Assistant for Z

Watsonx Code Assistant for Z operates on IBM's 20 billion-parameter model, which is set to be among the largest generative AI foundation models dedicated to code automation. IBM believes that by transitioning their mainframe apps from COBOL to Java, organizations can tap into a wider IT skill set and expedite developer integration. This is crucial as the COBOL language, despite being over 60 years old, still underpins many essential business processes globally. The challenge lies in the vast amount of COBOL-based applications that need to be restructured from the ground up. With Watsonx Code Assistant for Z, businesses can progressively convert COBOL apps into high-quality Java code, allowing developers to concentrate on adding new features.

Addressing the COBOL Challenge

IBM's new initiative leverages generative AI large language models (LLMs) to modernize these COBOL applications, especially on IBM System Z mainframes. The Watsonx Code Assistant for Z aims to bridge talent gaps, capitalize on Java expertise, and reduce associated risks.