Tang, Xunzhu and Tian, Haoye and Chen, Zhenghan and Pian, Weiguo and Ezzini, Saad and Kabore, Abdoul Kader and Habib, Andrew and Klein, Jacques and Bissyande, Tegawende F. (2024) Learning to Represent Patches. In: ICSE-Companion '24 : Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings. Proceedings - International Conference on Software Engineering . ACM, PRT, pp. 396-397. ISBN 9798400705021
Full text not available from this repository.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.