  Machine Learning for Software Engineering (ML4SE) is an actively growing
research area that focuses on methods that help programmers in their work. In
order to apply the developed methods in practice, they need to achieve
reasonable quality in order to help rather than distract developers. While the
development of new approaches to code representation and data collection
improves the overall quality of the models, it does not take into account the
information that we can get from the project at hand.
  In this work, we investigate how the model's quality can be improved if we
target a specific project. We develop a framework to assess quality
improvements that models can get after fine-tuning for the method name
prediction task on a particular project. We evaluate three models of different
complexity and compare their quality in three settings: trained on a large
dataset of Java projects, further fine-tuned on the data from a particular
project, and trained from scratch on this data. We show that per-project
fine-tuning can greatly improve the models' quality as they capture the
project's domain and naming conventions. We open-source the tool we used for
data collection, as well as the code to run the experiments:
https://zenodo.org/record/6040745.
