Automated Variable Renaming: Are We There Yet?

TL;DR

This paper evaluates the effectiveness of data-driven techniques for automated variable renaming, demonstrating their potential and limitations through large-scale experiments with state-of-the-art models.

Contribution

It provides the first large-scale empirical study comparing three advanced data-driven models for automated variable renaming.

Findings

Models can provide valuable renaming suggestions under certain conditions.

Current approaches have notable limitations that require further research.

The study offers insights into integrating these techniques into refactoring tools.

Abstract

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.