  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.
