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2022

MIXCODE: Enhancing Code Classification by Mixup-Based Data Augmentation

Dong, Zeming, Hu, Qiang, Guo, Yuejun et al.

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Inspired by the great success of Deep Neural Networks (DNNs) in natural language processing (NLP), DNNs have been increasingly applied in source code analysis and attracted significant attention from the software engineering community.

Due to its data-driven nature, a DNN model requires massive and high-quality labeled training data to achieve expert-level performance.
Collecting such data is often not hard, but the labeling process is notoriously laborious.
The task of DNN-based code analysis even worsens the situation because source code labeling also demands sophisticated expertise.

MIXCODE: Enhancing Code Classification by Mixup-Based Data Augmentation · Around

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