  The ability to adapt to unseen, local contexts is an important challenge that
successful models of source code must overcome. One of the most popular
approaches for the adaptation of such models is dynamic evaluation. With
dynamic evaluation, when running a model on an unseen file, the model is
updated immediately after having observed each token in that file. In this
work, we propose instead to frame the problem of context adaptation as a
meta-learning problem. We aim to train a base source code model that is best
able to learn from information in a file to deliver improved predictions of
missing tokens. Unlike dynamic evaluation, this formulation allows us to select
more targeted information (support tokens) for adaptation, that is both before
and after a target hole in a file. We consider an evaluation setting that we
call line-level maintenance, designed to reflect the downstream task of code
auto-completion in an IDE. Leveraging recent developments in meta-learning such
as first-order MAML and Reptile, we demonstrate improved performance in
experiments on a large scale Java GitHub corpus, compared to other adaptation
baselines including dynamic evaluation. Moreover, our analysis shows that,
compared to a non-adaptive baseline, our approach improves performance on
identifiers and literals by 44\% and 15\%, respectively.
