Comparing LLM-Based and Model-Driven Program Translation

Deep learning (DL) approaches for program translation have been extensively researched over recent years. These have been shown to be an effective alternative to manually-coded translators. At the same time, questions remain regarding the reliability and verifiability of DL approaches. Precise program translation approaches based on model-driven reverse engineering (MDRE) have also been researched. In this paper we provide the first detailed comparison of the results of the DL and MDRE approaches on tasks of COBOL translation to Python, Java and C\#.