AVATAR: A Parallel Corpus for Java-Python Program Translation

Wasi Uddin Ahmad, Md Golam Rahman Tushar, Saikat Chakraborty, Kai-Wei Chang

Abstract
Program translation refers to migrating source code from one programming language to another. It has tremendous practical value in software development, as porting software across languages is time-consuming and costly. Automating program translation is of paramount importance in software migration, and recently researchers explored unsupervised approaches due to the unavailability of parallel corpora. However, the availability of pre-trained language models for programming languages enables supervised fine-tuning with a small number of labeled examples. Therefore, we present AVATAR, a collection of 9,515 programming problems and their solutions written in two popular languages, Java and Python. AVATAR is collected from competitive programming sites, online platforms, and open-source repositories. Furthermore, AVATAR includes unit tests for 250 examples to facilitate functional correctness evaluation. We benchmark several pre-trained language models fine-tuned on AVATAR. Experiment results show that the models lack in generating functionally accurate code.
Anthology ID:
2023.findings-acl.143
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2268–2281
Language:
URL:
https://aclanthology.org/2023.findings-acl.143
DOI:
10.18653/v1/2023.findings-acl.143
Bibkey:
Cite (ACL):
Wasi Uddin Ahmad, Md Golam Rahman Tushar, Saikat Chakraborty, and Kai-Wei Chang. 2023. AVATAR: A Parallel Corpus for Java-Python Program Translation. In Findings of the Association for Computational Linguistics: ACL 2023, pages 2268–2281, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
AVATAR: A Parallel Corpus for Java-Python Program Translation (Ahmad et al., Findings 2023)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/2023.findings-acl.143.pdf
PDF Search