Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering |
| Subtitle of host publication | Companion Proceedings |
| Publisher | IEEE Computer Society |
| Pages | 352-353 |
| Number of pages | 2 |
| ISBN (electronic) | 9798400705021 |
| Publication status | Published - 23 May 2024 |
| Event | 46th International Conference on Software Engineering: Companion, ICSE-Companion 2024 - Lisbon, Portugal Duration: 14 Apr 2024 → 20 Apr 2024 |
Abstract
Supervised learning is a robust strategy for data-driven program translation. This work addresses the challenge of insufficient parallel training data in code translation by exploring two innovative data augmentation methods: a rule-based approach specifically designed for code translation datasets and a retrieval-based method leveraging unorganized code repositories.
ASJC Scopus subject areas
- Computer Science(all)
- Software
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Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings. IEEE Computer Society, 2024. p. 352-353.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Towards Data Augmentation for Supervised Code Translation
AU - Chen, Binger
AU - Golebiowski, Jacek
AU - Abedjan, Ziawasch
N1 - Publisher Copyright: © 2024 IEEE Computer Society. All rights reserved.
PY - 2024/5/23
Y1 - 2024/5/23
N2 - Supervised learning is a robust strategy for data-driven program translation. This work addresses the challenge of insufficient parallel training data in code translation by exploring two innovative data augmentation methods: a rule-based approach specifically designed for code translation datasets and a retrieval-based method leveraging unorganized code repositories.
AB - Supervised learning is a robust strategy for data-driven program translation. This work addresses the challenge of insufficient parallel training data in code translation by exploring two innovative data augmentation methods: a rule-based approach specifically designed for code translation datasets and a retrieval-based method leveraging unorganized code repositories.
UR - http://www.scopus.com/inward/record.url?scp=85194884166&partnerID=8YFLogxK
U2 - 10.1145/3639478.3643115
DO - 10.1145/3639478.3643115
M3 - Conference contribution
AN - SCOPUS:85194884166
SP - 352
EP - 353
BT - Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering
PB - IEEE Computer Society
T2 - 46th International Conference on Software Engineering: Companion, ICSE-Companion 2024
Y2 - 14 April 2024 through 20 April 2024
ER -