Automating software translation between programming languages remains a complex problem, primarily due to the challenges of re-expressing behavior while adhering to idiomatic constructs of a target language. The introduction of program skeletons offers a systematic framework to tackle this issue, retaining high-level structures while abstracting away language-specific details. This can lead to enhanced automation, allowing for more efficient translation processes across programming languages.
The prototype system Skel demonstrates this approach by translating Python code to JavaScript. With results showing that 95% of code fragments from selected real-world programs can be translated automatically, the potential for reducing manual efforts in translation is significant. However, the 5% of code that still requires manual intervention highlights ongoing challenges in achieving full automation. As the field progresses, the implications of this development could reshape software development practices by streamlining language transitions and improving overall code accessibility.
👉 Pročitaj original: arXiv AI Papers
