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Machine learning models that take computer program source code as inputtypically use Natural Language Processing (NLP) techniques. However, a majorchallenge is that code is written using an open, rapidly changing vocabularydue to, e.g., the coinage of new variable and method names. Reasoning over sucha vocabulary is not something for which most NLP methods are designed. Weintroduce a Graph-Structured Cache to address this problem; this cache containsa node for each new word the model encounters with edges connecting each wordto its occurrences in the code. We find that combining this graph-structuredcache strategy with recent Graph-Neural-Network-based models for supervisedlearning on code improves the models' performance on a code completion task anda variable naming task --- with over $100\%$ relative improvement on the latter--- at the cost of a moderate increase in computation time.

Open Vocabulary Learning on Source Code with a Graph-Structured Cache