AI vs. Dinosaurs – Automated Re-implementation of Legacy Mainframe Applications in Java by Combining Program Synthesis and GPT

Large-scale mainframe applications written in outdated languages such as COBOL still form the core of the enterprise IT in many organizations, even though their flexibility and maintainability declines continuously. Their manual re-implementation in modern languages like Java is usually economically not feasible. Automated code conversion of legacy programs usually produces poor quality code in the target language, even with recent AI tools such as ChatGPT. In addition, code conversion recovers dead or unnecessary code artifacts in the new language. Therefore, in this paper we explore a novel approach, which does not convert the legacy code, but instead uses the existing input/output data to generate program tokens through program synthesis. These tokens are subsequently translated into input tokens and submitted to ChatGPT to produce the target code. The approach is illustrated and evaluated by means of a semi-realistic example program. The obtained results look promising, but need to be further investigated.