There has been a steadily growing interest in development of novel methods to learn a representation of a given input data and subsequently using them for several downstream tasks. The field of natural language processing has seen a significant improvement in different tasks by incorporating pre-trained embeddings into their pipelines. Recently, these methods have been applied to programming languages with a view to improve developer productivity. In this paper, we present an unsupervised learning approach to encode old mainframe languages into a fixed dimensional vector space. We use COBOL as our motivating example and create a corpus and demonstrate the efficacy of our approach in a code-retrieval task on our corpus.

翻译：自然语言处理领域通过将预先培训的嵌入纳入编程管道,使不同的任务大有改进;最近,这些方法已应用于编程语言,以提高开发者的生产力;在本文件中,我们提出了一种未经监督的学习方法,将旧主机语言编码成一个固定的维向量空间。我们利用COBOL作为我们的激励榜样,并创建了一套材料,展示了我们在编程代码检索任务中的方法的有效性。