As the need for large-scale data processing grows, distributed programming frameworks like PySpark have become increasingly popular. However, the task of converting traditional, sequential code to distributed code remains a significant hurdle, often requiring specialized knowledge and substantial time investment. While existing tools have made strides in automating this conversion, they often fall short in terms of speed, flexibility, and overall applicability. In this paper, we introduce ROOP, a groundbreaking tool designed to address these challenges. Utilizing a BERT-based Natural Language Processing (NLP) model, ROOP automates the translation of Python code to its PySpark equivalent, offering a streamlined solution for leveraging distributed computing resources. We evaluated ROOP using a diverse set of 14 Python programs comprising 26 loop fragments. Our results are promising: ROOP achieved a near-perfect translation accuracy rate, successfully converting 25 out of the 26 loop fragments. Notably, for simpler operations, ROOP demonstrated remarkable efficiency, completing translations in as little as 44 seconds. Moreover, ROOP incorporates a built-in testing mechanism to ensure the functional equivalence of the original and translated code, adding an extra layer of reliability. This research opens up new avenues for automating the transition from sequential to distributed programming, making the process more accessible and efficient for developers.

翻译：随着大规模数据处理需求的增长，PySpark等分布式编程框架日益普及。然而，将传统的顺序代码转换为分布式代码仍然是一个重大障碍，通常需要专业知识和大量时间投入。尽管现有工具在自动化转换方面取得了进展，但在速度、灵活性和整体适用性方面仍存在不足。本文介绍了ROOP，这是一种旨在应对这些挑战的开创性工具。ROOP利用基于BERT的自然语言处理（NLP）模型，自动将Python代码翻译为等效的PySpark代码，为利用分布式计算资源提供了简化的解决方案。我们使用包含26个循环片段的14个不同Python程序对ROOP进行了评估。结果令人鼓舞：ROOP实现了接近完美的翻译准确率，成功转换了26个循环片段中的25个。值得注意的是，对于简单操作，ROOP表现出显著的效率，最快可在44秒内完成翻译。此外，ROOP内置了测试机制，以确保原始代码与翻译后代码的功能等价性，从而增加了额外的可靠性。这项研究为自动化从顺序编程到分布式编程的转换开辟了新途径，使开发人员能够更便捷、高效地完成这一过程。