AUVANA: An Efficient and Automatic Approach to Variable Rename Refactoring via Large Pre-trained Language Model

Download PDF
Open Webpage

Shiqi Cheng, Chenjie Shen, Li Yang, Lei Yu, Fengjun Zhang, Chun Zuo

Published: 2025, Last Modified: 23 Dec 2025ISSRE 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Rename refactoring is an essential practice in software maintenance, and Variable Rename Refactoring (VRR) is much more challenging than other types of identifiers. Meaningful variable names are critical for code readability and maintainability, as inconsistent variable names can hinder developers from comprehending code. Existing VRR research primarily focuses on Variable Name Consistency Checking (VCC) or variable name recommendation independently, but merely checking inconsistencies or recommending variable names is insufficient: a fully automated process must identify inconsistent names and then rectify them.In this paper, we propose AUVANA, a novel language model based framework to fully AUtomate VAriable reNAme refactoring that automates VRR by integrating inconsistency detection and meaningful variable name generation in Java. Unlike rule-based or semi-automatic approaches, AUVANA eliminates manual effort through two synergistic components: 1) a VCC model that identifies inconsistent variable names and 2) a Variable Name Refactoring (VNR) model that generates consistent replacements. To bridge the gap between pre-training and fine-tuning, we leverage prompt-tuning to improve model performance and tackle the challenge of multiple variable name occurrences. Hard negatives are introduced to address data scarcity.Experimental results demonstrate that AUVANA outperforms SoTA methods. On JavaRef and TL-CodeSum datasets, AUVANA achieves 57.8% and 56.1% Exact Match (EM) accuracy for VNR, exceeding prior baselines by 7.64% and 5.65%, respectively. For VCC, AUVANA attains 95.6% and 94.8% overall accuracy on JavaRef and TL-CodeSum, respectively, showcasing its ability to accurately detect inconsistent variable names. User study demonstrates that AUVANA VRR performance surpasses human in efficiency, precision and EM Accuracy. Artifacts are released to support future research.