  Despite the huge success of Large Language Models (LLMs) in coding assistants
like GitHub Copilot, these models struggle to understand the context present in
the repository (e.g., imports, parent classes, files with similar names, etc.),
thereby producing inaccurate code completions. This effect is more pronounced
when using these assistants for repositories that the model has not seen during
training, such as proprietary software or work-in-progress code projects.
Recent work has shown the promise of using context from the repository during
inference. In this work, we extend this idea and propose RepoFusion, a
framework to train models to incorporate relevant repository context.
Experiments on single-line code completion show that our models trained with
repository context significantly outperform much larger code models as
CodeGen-16B-multi ($\sim73\times$ larger) and closely match the performance of
the $\sim 70\times$ larger StarCoderBase model that was trained with the
Fill-in-the-Middle objective. We find these results to be a novel and
compelling demonstration of the gains that training with repository context can
bring. We carry out extensive ablation studies to investigate the impact of
design choices such as context type, number of contexts, context length, and
initialization within our framework. Lastly, we release Stack-Repo, a dataset
of 200 Java repositories with permissive licenses and near-deduplicated files
that are augmented with three types of repository contexts. Additionally, we
are making available the code and trained checkpoints for our work. Our
released resources can be found at \url{https://huggingface.co/RepoFusion}.
