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

Nadezhda Chirkova; Sergey Troshin

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arxiv 2010.12663 v2 pith:5AEWQ7VW submitted 2020-10-23 cs.SE cs.LG

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

Nadezhda Chirkova , Sergey Troshin This is my paper

classification cs.SE cs.LG

keywords codeidentifiersmethodprocessingsourceanonymizationdeeplearning

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There is an emerging interest in the application of natural language processing models to source code processing tasks. One of the major problems in applying deep learning to software engineering is that source code often contains a lot of rare identifiers, resulting in huge vocabularies. We propose a simple, yet effective method, based on identifier anonymization, to handle out-of-vocabulary (OOV) identifiers. Our method can be treated as a preprocessing step and, therefore, allows for easy implementation. We show that the proposed OOV anonymization method significantly improves the performance of the 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

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