Learning to Represent Patches

Xunzhu Tang, Haoye Tian, Zhenghan Chen, Weiguo Pian, Saad Ezzini, Abdoul Kader Kaboré, Andrew Habib, Jacques Klein, Tegawendé F. Bissyandé

Abstract: We propose Patcherizer, a novel patch representation methodology that combines context and structure intention features to capture the semantic changes in Abstract Syntax Trees (ASTs) and surrounding context of code changes. Utilizing graph convolutional neural networks and transformers, Patcherizer effectively captures the underlying intentions of patches, outperforming state-of-the-art representations with significant improvements in BLEU, ROUGE-L, and METEOR metrics for generating patch descriptions.