Neural Variable Name Repair: Learning to Rename Identifiers for Readability

TL;DR

This paper introduces a neural approach to automatically repair and rename misleading variable identifiers in C++ code, significantly improving code readability and understanding by leveraging fine-tuned language models and reranking techniques.

Contribution

The paper presents a novel neural method for variable name repair using fine-tuned Llama models, dual-encoder rerankers, and a new evaluation metric for near-synonym recognition.

Findings

Best model achieves 43.1% exact match in variable repair

Reranker improves selection quality without retraining generator

Partial-match score indicates high potential for near-synonym repairs

Abstract

Developers routinely work with source files whose variable names are generic or misleading, and with teams moving quickly, many functions are left undocumented. This slows comprehension, increases the risk of subtle bugs, and makes it harder for both humans and large language models (LLMs) to reason about code. We study variable name repair: given a real C++ function where all occurrences of one local or parameter name have been replaced by a placeholder (e.g. ID 1), the goal is to generate a natural, descriptive replacement name. We automatically construct this task from the C++ portion of BigCode's The Stack by parsing functions with Tree-sitter, masking a single identifier, and treating the original name as supervision. On top of Llama 3.1-8B, we build a pipeline with (i) warmup and dropout schedules for more stable fine-tuning, (ii) LoRA adapters for efficient specialization on identifier repair, and (iii) a dual-encoder reranker over top-k generator candidates. We evaluate using exact match, Top-5 Hit, and an embedding-based partial similarity score (0-100) that gives credit for near synonyms and format variants (e.g., jsonValue vs. json). On a held-out set of 200 C++ functions, a zero-shot Llama 3.1 baseline reaches 6.1 percent exact match. Our best LoRA-tuned model (with warmup and dropout) achieves 43.1 percent exact match, 50.2 percent Top-5 Hit, and an 82.03 partial-match score. A dual encoder reranker further improves selection quality without modifying the underlying generator, suggesting that task-specific fine-tuning plus reranking is a promising approach for practical identifier repair tools.