  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.
