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There is an emerging interest in the application of natural languageprocessing models to source code processing tasks. One of the major problems inapplying deep learning to software engineering is that source code oftencontains a lot of rare identifiers, resulting in huge vocabularies. We proposea simple, yet effective method, based on identifier anonymization, to handleout-of-vocabulary (OOV) identifiers. Our method can be treated as apreprocessing step and, therefore, allows for easy implementation. We show thatthe proposed OOV anonymization method significantly improves the performance ofthe Transformer in two code processing tasks: code completion and bug fixing.

A Simple Approach for Handling Out-of-Vocabulary Identifiers in Deep Learning for Source Code