Machine learning models that take computer program source code as input
typically use Natural Language Processing (NLP) techniques. However, a major
challenge is that code is written using an open, rapidly changing vocabulary
due to, e.g., the coinage of new variable and method names. Reasoning over such
a vocabulary is not something for which most NLP methods are designed. We
introduce a Graph-Structured Cache to address this problem; this cache contains
a node for each new word the model encounters with edges connecting each word
to its occurrences in the code. We find that combining this graph-structured
cache strategy with recent Graph-Neural-Network-based models for supervised
learning on code improves the models' performance on a code completion task and
a variable naming task --- with over 100% relative improvement on the latter
--- at the cost of a moderate increase in computation time.Comment: Published in the International Conference on Machine Learning (ICML
2019), 13 page