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Automated translation of legacy software into modern languages is essential for adopting safer programming practices at scale. We review current rule-based, neural and neurosymbolic approaches and show why none fully address the needs of real-world repositories. Rule-based tools scale but mirror the source language, producing unidiomatic target code. LLM-based methods capture idioms but lack correctness guarantees. Hybrid systems partially bridge the gap but remain brittle when faced with complex features such as concurrency or third-party dependencies.

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Automated Translation of Real-World Codebases: How Far Are We?

  • 会议论文

  • 发表日期：

    2026年

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  • Cristina David,
  • Hanliang Zhang,
  • Meng Wang

摘要

Automated translation of legacy software into modern languages is essential for adopting safer programming practices at scale. We review current rule-based, neural and neurosymbolic approaches and show why none fully address the needs of real-world repositories. Rule-based tools scale but mirror the source language, producing unidiomatic target code. LLM-based methods capture idioms but lack correctness guarantees. Hybrid systems partially bridge the gap but remain brittle when faced with complex features such as concurrency or third-party dependencies.