论文标题
代码翻译的质量估计和解释性
Quality Estimation & Interpretability for Code Translation
论文作者
论文摘要
最近，通过使用灵感来自神经机器翻译（NMT）自然语言的自动方法，将源代码从一种编程语言转换为另一种编程语言。但是，这种方法与以前关于自然语言的NMT方法相同的问题，即。缺乏估计和评估翻译质量的能力；因此，将某种解释性归因于模型的选择。在本文中，我们试图估算基于转码模型顶部的源代码翻译的质量。我们将代码翻译任务视为自然语言的机器翻译（MT）的模拟，并增加了一些警告。我们从围绕代码翻译构建的用户研究中介绍了我们的主要动机；并提出了一种将该模型产生的信心与翻译代码中的棉棉错误相关的技术。我们以对这些相关性的一些观察以及对未来工作的一些想法进行了结论。
Recently, the automated translation of source code from one programming language to another by using automatic approaches inspired by Neural Machine Translation (NMT) methods for natural languages has come under study. However, such approaches suffer from the same problem as previous NMT approaches on natural languages, viz. the lack of an ability to estimate and evaluate the quality of the translations; and consequently ascribe some measure of interpretability to the model's choices. In this paper, we attempt to estimate the quality of source code translations built on top of the TransCoder model. We consider the code translation task as an analog of machine translation (MT) for natural languages, with some added caveats. We present our main motivation from a user study built around code translation; and present a technique that correlates the confidences generated by that model to lint errors in the translated code. We conclude with some observations on these correlations, and some ideas for future work.
