terryyz/DataAug4Code
Source Code Data Augmentation for Deep Learning: A Survey.
This is a curated list of research papers and their associated datasets focused on using data augmentation techniques for training deep learning models on source code. It helps researchers and practitioners explore different methods for tasks like identifying code authorship, detecting code clones, finding and fixing defects, or summarizing code. Anyone working on improving automated code analysis or generation with machine learning would find this useful.
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Use this if you are a researcher or machine learning engineer looking for state-of-the-art data augmentation techniques and relevant papers to enhance your deep learning models for source code tasks.
Not ideal if you are looking for an off-the-shelf tool or library to directly apply data augmentation, as this is a survey of academic literature, not an implementation.
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Jun 15, 2024
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