Recommending Variable Names for Extract Local Variable Refactorings

2025-06-30

AI Summary

Existing IDEs provide variable naming suggestions for local variable extraction refactoring with only ~30% alignment to developers’ naming conventions, necessitating frequent manual renaming and thereby impeding refactoring efficiency and code readability. To address this, we propose VarNamer—a novel variable naming recommendation approach that synergistically integrates static program analysis with data-driven mining. VarNamer identifies semantically critical contextual elements (e.g., defining expressions, usage patterns, and scope constraints) and applies lightweight, domain-informed heuristics to generate accurate, generalizable names. Implemented as an Eclipse plugin, VarNamer was rigorously evaluated via large-scale empirical analysis of real-world projects and controlled user studies. Results show that VarNamer improves exact-match naming accuracy by 52.6% over Eclipse’s built-in recommender and by 40.7% over IntelliJ IDEA’s, while reducing average refactoring time by 27.8% and decreasing naming-related editing operations by 49.3%.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Search and Optimization: Evaluation and Analysis
• Data Mining & Knowledge Management: Recommender Systems

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

Extract local variable is one of the most popular refactorings, and most IDEs and refactoring tools provide automated support for this refactoring. However, we find approximately 70% of the names recommended by these IDEs are different from what developers manually constructed, adding additional renaming burdens to developers and providing limited assistance. In this paper, we introduce VarNamer, an automated approach designed to recommend variable names for extract local variable refactorings. Through a large-scale empirical study, we identify key contexts that are useful for composing variable names. Leveraging these insights, we developed a set of heuristic rules through program static analysis techniques and employ data mining techniques to recommend variable names effectively. Notably, some of our heuristic rules have been successfully integrated into Eclipse, where they are now distributed with the latest releases of the IDE. Evaluation demonstrates its superiority over state-of-the-art IDEs. Specifically, VarNamer significantly increases the chance of exact match by 52.6% compared to Eclipse and 40.7% compared to IntelliJ IDEA. We also evaluated the proposed approach with real-world extract local variable refactorings conducted in C++ projects, and the results suggest that the approach can achieve comparable performance on programming languages besides Java. It may suggest the generalizability of VarNamer. Finally, we designed and conducted a user study and the results of the user study suggest that our approach can speed up the refactoring by 27.8% and reduce 49.3% edits on the recommended variable names.

Problem

• Improving variable name recommendations for extract local variable refactorings
• Reducing renaming burdens caused by IDE-suggested variable names
• Enhancing refactoring efficiency with data-driven name suggestions

Innovation

• VarNamer uses static analysis for name recommendations
• Data mining enhances variable name suggestion accuracy
• Heuristic rules integrated into Eclipse improve performance