  In recent years, Neural Machine Translator (NMT) has shown promise in
automatically editing source code. Typical NMT based code editor only considers
the code that needs to be changed as input and suggests developers with a
ranked list of patched code to choose from - where the correct one may not
always be at the top of the list. While NMT based code editing systems generate
a broad spectrum of plausible patches, the correct one depends on the
developers' requirement and often on the context where the patch is applied.
Thus, if developers provide some hints, using natural language, or providing
patch context, NMT models can benefit from them. As a proof of concept, in this
research, we leverage three modalities of information: edit location, edit code
context, commit messages (as a proxy of developers' hint in natural language)
to automatically generate edits with NMT models. To that end, we build MODIT, a
multi-modal NMT based code editing engine. With in-depth investigation and
analysis, we show that developers' hint as an input modality can narrow the
search space for patches and outperform state-of-the-art models to generate
correctly patched code in top-1 position.
