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Open Vocabulary Learning on Source Code with a Graph-Structured Cache
Milan Cvitkovic · Badal Singh · Anima Anandkumar

Wed Jun 12 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #176

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.

Author Information

Milan Cvitkovic (California Institute of Technology)
Badal Singh (Amazon Web Services)
Anima Anandkumar (Caltech)

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