  Identifiers, such as method and variable names, form a large portion of
source code. Therefore, low-quality identifiers can substantially hinder code
comprehension. To support developers in using meaningful identifiers, several
(semi-)automatic techniques have been proposed, mostly being data-driven (e.g.
statistical language models, deep learning models) or relying on static code
analysis. Still, limited empirical investigations have been performed on the
effectiveness of such techniques for recommending developers with meaningful
identifiers, possibly resulting in rename refactoring operations. We present a
large-scale study investigating the potential of data-driven approaches to
support automated variable renaming. We experiment with three state-of-the-art
techniques: a statistical language model and two DL-based models. The three
approaches have been trained and tested on three datasets we built with the
goal of evaluating their ability to recommend meaningful variable identifiers.
Our quantitative and qualitative analyses show the potential of such techniques
that, under specific conditions, can provide valuable recommendations and are
ready to be integrated in rename refactoring tools. Nonetheless, our results
also highlight limitations of the experimented approaches that call for further
research in this field.
