  Recently, the automated translation of source code from one programming
language to another by using automatic approaches inspired by Neural Machine
Translation (NMT) methods for natural languages has come under study. However,
such approaches suffer from the same problem as previous NMT approaches on
natural languages, viz. the lack of an ability to estimate and evaluate the
quality of the translations; and consequently ascribe some measure of
interpretability to the model's choices. In this paper, we attempt to estimate
the quality of source code translations built on top of the TransCoder model.
We consider the code translation task as an analog of machine translation (MT)
for natural languages, with some added caveats. We present our main motivation
from a user study built around code translation; and present a technique that
correlates the confidences generated by that model to lint errors in the
translated code. We conclude with some observations on these correlations, and
some ideas for future work.
