Generative AI for Code Translation: A Systematic Mapping Study
Aymane Rgaguena, Imane Chlioui, Maryam Radgui · 2025
Generative artificial intelligence (AI) has greatly advanced the code translation process, particularly through large language models (LLMs), which translate source code from one programming language into another. This translation has historically been error-prone, labor-intensive, and highly dependent on manual intervention. Although traditional tools such as compilers and transpilers have restrictions in managing complex programming paradigms, recent generative AI models most importantly those based on transformer architectures, have shown promise. This systematic mapping study intends to evaluate and compile studies on Generative AI applications in code translation released between 2020 and 2025. Using five main criteria, the publication year and channel, research type, publication type, empirical study type, and AI models. A total of 53 relevant articles were chosen and examined. The results show that conferences and journals are the most often used publishing venues. Although historical-based evaluations and case studies were the empirical methodologies most often used, researchers have primarily focused on implementing transformer-based artificial intelligence models.