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.

This paper provides a starting point for Software Engineering (SE) researchers and practitioners faced with the problem of training machine learning models on small datasets. Due to the high costs associated with labeling data, in Software Engineering, there exist many small (< 5,000 samples) and medium-sized (<100,000 samples) datasets. While deep learning has set the state of the art in many machine learning tasks, it is only recently that it has proven effective on small-sized datasets, primarily thanks to pre-training, a semi-supervised learning technique that leverages abundant unlabelled data alongside scarce labelled data. In this work, we evaluate pre-trained Transformer models on a selection of 13 smaller datasets from the SE literature, covering both, source code and natural language. Our results suggest that pre-trained Transformers are competitive and in some cases superior to previous models, especially for tasks involving natural language; whereas for source code tasks, in particular for very small datasets, traditional machine learning methods often has the edge. In addition, we experiment with several techniques that ought to aid training on small datasets, including active learning, data augmentation, soft labels, self-training and intermediate-task fine-tuning, and issue recommendations on when they are effective. We also release all the data, scripts, and most importantly pre-trained models for the community to reuse on their own datasets.