  Deep learning source code models have been applied very successfully to the
problem of automated program repair. One of the standing issues is the small
input window of current models which often cannot fully fit the context code
required for a bug fix (e.g., method or class declarations of a project).
Instead, input is often restricted to the local context, that is, the lines
below and above the bug location. In this work we study the importance of this
local context on repair success: how much local context is needed?; is context
before or after the bug location more important? how is local context tied to
the bug type? To answer these questions we train and evaluate Transformer
models in many different local context configurations on three datasets and two
programming languages. Our results indicate that overall repair success
increases with the size of the local context (albeit not for all bug types) and
confirm the common practice that roughly 50-60% of the input window should be
used for context leading the bug. Our results are not only relevant for
researchers working on Transformer-based APR tools but also for benchmark and
dataset creators who must decide what and how much context to include in their
datasets.
