A Detailed Survey on Machine Learning-Based Programming Language Translation Systems

Abstract

The rapid evolution of programming languages and their diverse paradigms has led to the need for effective tools that can bridge the gap between different languages. Traditional programming language translators (compilers and interpreters) often require significant manual effort for syntax and semantic mapping, which can be time- consuming and error-prone. Recent advancements in machine learning (ML) offer promising solutions for automating and optimizing the process of language translation. This paper provides a comprehensive survey on machine learning-based programming language translation systems, exploring various approaches, techniques, and tools that leverage ML algorithms to translate code from one programming language to another. The survey covers key areas such as supervised learning, unsupervised learning, neural networks, and reinforcement learning, along with their applications in source code analysis, transformation, and optimization. We also discuss the challenges faced by these systems, including accuracy, scalability, and handling language-specific semantics, as well as their potential to enhance existing software development workflows. Finally, the paper outlines the future directions of ML-driven programming language translators, including the integration of more sophisticated AI techniques and cross-paradigm translation, which could revolutionize software development and maintainability in the years to come.