# Learning Natural Coding Conventions

arXiv: https://arxiv.org/abs/1402.4182 · HTML: https://mlsanity.com/paper/1402.4182/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2014.

## Built on by (most cited first, 29 of 29)

- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
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- [Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review](https://mlsanity.com/paper/2307.02503/) (2023)
- [Aligning Offline Metrics and Human Judgments of Value for Code Generation Models](https://mlsanity.com/paper/2210.16494/) (2022)
- [MPCODER: Multi-user Personalized Code Generator with Explicit and Implicit Style Representation Learning](https://mlsanity.com/paper/2406.17255/) (2024)
- [VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning](https://mlsanity.com/paper/2112.02650/) (2021)
- [Copilot for Xcode: Exploring AI-Assisted Programming by Prompting Cloud-based Large Language Models](https://mlsanity.com/paper/2307.14349/) (2023)
- [Enhancing Automated Program Repair with Solution Design](https://mlsanity.com/paper/2408.12056/) (2024)
- [Variable Name Recovery in Decompiled Binary Code using Constrained Masked Language Modeling](https://mlsanity.com/paper/2103.12801/) (2021)
- [Syntax-Guided Program Reduction for Understanding Neural Code Intelligence Models](https://mlsanity.com/paper/2205.14374/) (2022)
- [Code2Snapshot: Using Code Snapshots for Learning Representations of Source Code](https://mlsanity.com/paper/2111.01097/) (2021)
- [AdaptivePaste: Code Adaptation through Learning Semantics-aware Variable Usage Representations](https://mlsanity.com/paper/2205.11023/) (2022)
- [NeuDep: Neural Binary Memory Dependence Analysis](https://mlsanity.com/paper/2210.02853/) (2022)
- [Structured Code Representations Enable Data-Efficient Adaptation of Code Language Models](https://mlsanity.com/paper/2401.10716/) (2024)
- [Deep Learning-Based Identification of Inconsistent Method Names: How Far Are We?](https://mlsanity.com/paper/2501.12617/) (2025)
- [Roosterize: Suggesting Lemma Names for Coq Verification Projects Using Deep Learning](https://mlsanity.com/paper/2103.01346/) (2021)
- [MLSmellHound: A Context-Aware Code Analysis Tool](https://mlsanity.com/paper/2205.03790/) (2022)
- [Out of style: Misadventures with LLMs and code style transfer](https://mlsanity.com/paper/2406.10320/) (2024)
- [Extracting Label-specific Key Input Features for Neural Code Intelligence Models](https://mlsanity.com/paper/2202.06474/) (2022)
- [Modelling Concurrency Bugs Using Machine Learning](https://mlsanity.com/paper/2305.05531/) (2023)
- [Epicure: Distilling Sequence Model Predictions into Patterns](https://mlsanity.com/paper/2308.08203/) (2023)
- [Untargeted Code Authorship Evasion with Seq2Seq Transformation](https://mlsanity.com/paper/2311.15366/) (2023)
- [Autonomous Vehicles: Evolution of Artificial Intelligence and Learning Algorithms](https://mlsanity.com/paper/2402.17690/) (2024)
- [Contextual Augmented Multi-Model Programming (CAMP): A Hybrid Local-Cloud Copilot Framework](https://mlsanity.com/paper/2410.15285/) (2024)
- [Still Manual? Automated Linter Configuration via DSL-Based LLM Compilation of Coding Standards](https://mlsanity.com/paper/2602.07783/) (2026)
- [The Code Whisperer: LLM and Graph-Based AI for Smell and Vulnerability Resolution](https://mlsanity.com/paper/2604.13114/) (2026)
- [Correct Code, Broken Contributions? SWE-CC: Benchmarking Repository Policy Compliance for Coding Agents](https://mlsanity.com/paper/2610.06193/) (2026)

## Abstract (arXiv)

Every programmer has a characteristic style, ranging from preferences about identifier naming to preferences about object relationships and design patterns. Coding conventions define a consistent syntactic style, fostering readability and hence maintainability. When collaborating, programmers strive to obey a project's coding conventions. However, one third of reviews of changes contain feedback about coding conventions, indicating that programmers do not always follow them and that project members care deeply about adherence. Unfortunately, programmers are often unaware of coding conventions because inferring them requires a global view, one that aggregates the many local decisions programmers make and identifies emergent consensus on style. We present NATURALIZE, a framework that learns the style of a codebase, and suggests revisions to improve stylistic consistency. NATURALIZE builds on recent work in applying statistical natural language processing to source code. We apply NATURALIZE to suggest natural identifier names and formatting conventions. We present four tools focused on ensuring natural code during development and release management, including code review. NATURALIZE achieves 94% accuracy in its top suggestions for identifier names and can even transfer knowledge about conventions across projects, leveraging a corpus of 10,968 open source projects. We used NATURALIZE to generate 18 patches for 5 open source projects: 14 were accepted.