Details
| Original language | English |
|---|---|
| Title of host publication | 2023 IEEE/ACM 45th International Conference on Software Engineering |
| Subtitle of host publication | ICSE 2023 |
| Publisher | IEEE Computer Society |
| Pages | 2362-2373 |
| Number of pages | 12 |
| ISBN (electronic) | 9781665457019 |
| ISBN (print) | 978-1-6654-5702-6 |
| Publication status | Published - 2023 |
| Event | 45th IEEE/ACM International Conference on Software Engineering - Melbourne, Australia Duration: 14 May 2023 → 20 May 2023 |
Publication series
| Name | Proceedings - International Conference on Software Engineering |
|---|---|
| ISSN (Print) | 0270-5257 |
Abstract
Coding style has direct impact on code comprehension. Automatically transferring code style to user's preference or consistency can facilitate project cooperation and maintenance, as well as maximize the value of open-source code. Existing work on automating code stylization is either limited to code formatting or requires human supervision in pre-defining style checking and transformation rules. In this paper, we present unsupervised methods to assist automatic code style transfer for arbitrary code styles. The main idea is to leverage Big Code database to learn style and content embedding separately to generate or retrieve a piece of code with the same functionality and the desired target style. We carefully encode style and content features, so that a style embedding can be learned from arbitrary code. We explored the capabilities of novel attention-based style generation models and meta-learning and implemented our ideas in DUETCS. We complement the learning-based approach with a retrieval mode, which uses the same embeddings to directly search for the desired piece of code in Big Code. Our experiments show that DUETCS captures more style aspects than existing baselines.
ASJC Scopus subject areas
- Computer Science(all)
- Software
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
2023 IEEE/ACM 45th International Conference on Software Engineering: ICSE 2023. IEEE Computer Society, 2023. p. 2362-2373 (Proceedings - International Conference on Software Engineering).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - DuetCS
T2 - 45th IEEE/ACM International Conference on Software Engineering
AU - Chen, Binger
AU - Abedjan, Ziawasch
N1 - Funding Information: This work was funded by the German Ministry for Education and Research as BIFOLD - Berlin Institute for the Foundations of Learning and Data (ref. 01IS18025A and ref. 01IS18037A).
PY - 2023
Y1 - 2023
N2 - Coding style has direct impact on code comprehension. Automatically transferring code style to user's preference or consistency can facilitate project cooperation and maintenance, as well as maximize the value of open-source code. Existing work on automating code stylization is either limited to code formatting or requires human supervision in pre-defining style checking and transformation rules. In this paper, we present unsupervised methods to assist automatic code style transfer for arbitrary code styles. The main idea is to leverage Big Code database to learn style and content embedding separately to generate or retrieve a piece of code with the same functionality and the desired target style. We carefully encode style and content features, so that a style embedding can be learned from arbitrary code. We explored the capabilities of novel attention-based style generation models and meta-learning and implemented our ideas in DUETCS. We complement the learning-based approach with a retrieval mode, which uses the same embeddings to directly search for the desired piece of code in Big Code. Our experiments show that DUETCS captures more style aspects than existing baselines.
AB - Coding style has direct impact on code comprehension. Automatically transferring code style to user's preference or consistency can facilitate project cooperation and maintenance, as well as maximize the value of open-source code. Existing work on automating code stylization is either limited to code formatting or requires human supervision in pre-defining style checking and transformation rules. In this paper, we present unsupervised methods to assist automatic code style transfer for arbitrary code styles. The main idea is to leverage Big Code database to learn style and content embedding separately to generate or retrieve a piece of code with the same functionality and the desired target style. We carefully encode style and content features, so that a style embedding can be learned from arbitrary code. We explored the capabilities of novel attention-based style generation models and meta-learning and implemented our ideas in DUETCS. We complement the learning-based approach with a retrieval mode, which uses the same embeddings to directly search for the desired piece of code in Big Code. Our experiments show that DUETCS captures more style aspects than existing baselines.
UR - http://www.scopus.com/inward/record.url?scp=85171738729&partnerID=8YFLogxK
U2 - 10.1109/ICSE48619.2023.00198
DO - 10.1109/ICSE48619.2023.00198
M3 - Conference contribution
AN - SCOPUS:85171738729
SN - 978-1-6654-5702-6
T3 - Proceedings - International Conference on Software Engineering
SP - 2362
EP - 2373
BT - 2023 IEEE/ACM 45th International Conference on Software Engineering
PB - IEEE Computer Society
Y2 - 14 May 2023 through 20 May 2023
ER -