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.
| Original language | English |
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| Title of host publication | Proceedings - 2024 ACM/IEEE 46th International Conference on Software Engineering |
| Subtitle of host publication | Companion, ICSE-Companion 2024 |
| Publisher | IEEE Computer Society |
| Pages | 396-397 |
| Number of pages | 2 |
| ISBN (Electronic) | 9798400705021 |
| DOIs | |
| State | Published - 23 May 2024 |
| Externally published | Yes |
| Event | 46th International Conference on Software Engineering: Companion, ICSE-Companion 2024 - Lisbon, Portugal Duration: 14 Apr 2024 → 20 Apr 2024 |
Publication series
| Name | Proceedings - International Conference on Software Engineering |
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| ISSN (Print) | 0270-5257 |
Conference
| Conference | 46th International Conference on Software Engineering: Companion, ICSE-Companion 2024 |
|---|---|
| Country/Territory | Portugal |
| City | Lisbon |
| Period | 14/04/24 → 20/04/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE Computer Society. All rights reserved.
ASJC Scopus subject areas
- Software