# code_transformed: The Influence of Large Language Models on Code

arXiv: https://arxiv.org/abs/2506.12014 · HTML: https://mlsanity.com/paper/2506.12014/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 53 references that are in the map.
- First posted on arXiv 13 Jun 2025.

## Builds on (most cited first, 23 of 23)

- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://mlsanity.com/paper/2305.01210/) (2023)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021)
- [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021)
- [A Survey on Large Language Models for Code Generation](https://mlsanity.com/paper/2406.00515/) (2024)
- [Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](https://mlsanity.com/paper/2108.09293/) (2021)
- [Can ChatGPT replace StackOverflow? A Study on Robustness and Reliability of Large Language Model Code Generation](https://mlsanity.com/paper/2308.10335/) (2023)
- [CodeScore: Evaluating Code Generation by Learning Code Execution](https://mlsanity.com/paper/2301.09043/) (2023)
- [Bugs in Large Language Models Generated Code: An Empirical Study](https://mlsanity.com/paper/2403.08937/) (2024)
- [What's Wrong with Your Code Generated by Large Language Models? An Extensive Study](https://mlsanity.com/paper/2407.06153/) (2024)
- [What Makes Large Language Models Reason in (Multi-Turn) Code Generation?](https://mlsanity.com/paper/2410.08105/) (2024)
- [An Empirical Study on Usage and Perceptions of LLMs in a Software Engineering Project](https://mlsanity.com/paper/2401.16186/) (2024)
- [Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human Programmers](https://mlsanity.com/paper/2401.06461/) (2024)
- [The Impact of Large Language Models in Academia: from Writing to Speaking](https://mlsanity.com/paper/2409.13686/) (2024)
- [Distinguishing LLM-generated from Human-written Code by Contrastive Learning](https://mlsanity.com/paper/2411.04704/) (2024)
- [LiCoEval: Evaluating LLMs on License Compliance in Code Generation](https://mlsanity.com/paper/2408.02487/) (2024)
- [Does Your Neural Code Completion Model Use My Code? A Membership Inference Approach](https://mlsanity.com/paper/2404.14296/) (2024)
- [Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting](https://mlsanity.com/paper/2405.16133/) (2024)
- [Is This You, LLM? Recognizing AI-written Programs with Multilingual Code Stylometry](https://mlsanity.com/paper/2412.14611/) (2024)
- [AIGCodeSet: A New Annotated Dataset for AI Generated Code Detection](https://mlsanity.com/paper/2412.16594/) (2024)
- [Detection of LLM-Paraphrased Code and Identification of the Responsible LLM Using Coding Style Features](https://mlsanity.com/paper/2502.17749/) (2025)

## Abstract (arXiv)

Coding remains one of the most fundamental modes of interaction between humans and machines. With the rapid advancement of Large Language Models (LLMs), code generation capabilities have begun to significantly reshape programming practices. This development prompts a central question: Have LLMs transformed code style, and how can such transformation be characterized? In this paper, we present a pioneering study that investigates the impact of LLMs on code style, with a focus on naming conventions, complexity, maintainability, and similarity. By analyzing code from over 20,000 GitHub repositories linked to arXiv papers published between 2020 and 2025, we identify measurable trends in the evolution of coding style that align with characteristics of LLM-generated code. For instance, the proportion of snake_case function names in Python code increased from 40.7% in Q1 2023 to 49.8% in Q3 2025. Furthermore, we investigate how LLMs approach algorithmic problems by examining their reasoning processes. Our experimental results may provide the first large-scale empirical evidence that LLMs affect real-world programming style. We release all the experimental dataset and source code at: https://github.com/ignorancex/LLM_code