Neural Machine Translation of Source Code Across Programming Languages Using Transformer Architecture

@article{1719036,
      author = {Pallavi Mahale},
      title = {Neural Machine Translation of Source Code Across Programming Languages Using Transformer Architecture},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {12},
      pages = {2039-2047},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1719036.pdf},
      abstract = {The automatic translation of source code between programming languages is a critical challenge in modern software engineering, particularly for legacy system migration and cross-platform development. This paper proposes a Transformer-based Neural Machine Translation (NMT) framework specifically designed for source-to-source code translation, targeting language pairs including Python↔Java, Python↔C++, and Java↔C++. Unlike traditional rule-based transpilers, our approach leverages pre-trained code models (CodeT5+) fine-tuned on a curated multilingual parallel corpus, augmented with Abstract Syntax Tree (AST) structural embeddings to better capture code semantics. We introduce a novel post-processing semantic validation module using unit-test execution feedback and compiler signals to iteratively refine translations and maximize functional equivalence. Experimental evaluation on standard benchmarks (TransCoder-test, AVATAR, CodeNet) demonstrates state-of-the-art Computational Accuracy (CA@1) scores and significant reductions in compilation errors compared to baseline models. Our work addresses the key open challenge of semantic preservation in neural code translation, contributing both a novel architecture and a new evaluation protocol.},
      keywords = {Neural Machine Translation, Source Code Translation, Transformer Architecture, CodeT5, Abstract Syntax Tree, Semantic Preservation, Legacy Migration},
      month = {June},
      doi = {https://doi.org/10.64388/IREV9I12-1719036}
  }