TITLE: Making the most of small Software Engineering datasets with modern machine learning 

ABSTRACT: 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 (< 1 000 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.
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 (< 1 000 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.
Making the most of small Software Engineering datasets with modern machine learning
http://arxiv.org/abs/2106.15209v1