# Context2Name: A Deep Learning-Based Approach to Infer Natural Variable Names from Usage Contexts

arXiv: https://arxiv.org/abs/1809.05193 · HTML: https://mlsanity.com/paper/1809.05193/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2018.

## Built on by (most cited first, 11 of 11)

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- [Self-Supervised Learning to Prove Equivalence Between Straight-Line Programs via Rewrite Rules](https://mlsanity.com/paper/2109.10476/) (2021)
- [AdaptivePaste: Code Adaptation through Learning Semantics-aware Variable Usage Representations](https://mlsanity.com/paper/2205.11023/) (2022)
- [Proving Equivalence Between Complex Expressions Using Graph-to-Sequence Neural Models](https://mlsanity.com/paper/2106.02452/) (2021)
- [Towards Top-Down Automated Development in Limited Scopes: A Neuro-Symbolic Framework from Expressibles to Executables](https://mlsanity.com/paper/2209.01566/) (2022)
- [Identifying Adversary Tactics and Techniques in Malware Binaries with an LLM Agent](https://mlsanity.com/paper/2602.06325/) (2026)
- [Epicure: Distilling Sequence Model Predictions into Patterns](https://mlsanity.com/paper/2308.08203/) (2023)
- [Neural Variable Name Repair: Learning to Rename Identifiers for Readability](https://mlsanity.com/paper/2512.01141/) (2025)

## Abstract (arXiv)

Most of the JavaScript code deployed in the wild has been minified, a process in which identifier names are replaced with short, arbitrary and meaningless names. Minified code occupies less space, but also makes the code extremely difficult to manually inspect and understand. This paper presents Context2Name, a deep learningbased technique that partially reverses the effect of minification by predicting natural identifier names for minified names. The core idea is to predict from the usage context of a variable a name that captures the meaning of the variable. The approach combines a lightweight, token-based static analysis with an auto-encoder neural network that summarizes usage contexts and a recurrent neural network that predict natural names for a given usage context. We evaluate Context2Name with a large corpus of real-world JavaScript code and show that it successfully predicts 47.5% of all minified identifiers while taking only 2.9 milliseconds on average to predict a name. A comparison with the state-of-the-art tools JSNice and JSNaughty shows that our approach performs comparably in terms of accuracy while improving in terms of efficiency. Moreover, Context2Name complements the state-of-the-art by predicting 5.3% additional identifiers that are missed by both existing tools.