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code_transformed: The Influence of Large Language Models on Code

arXiv 2506.12014 ↗First posted 13 Jun 2025cs.CL cs.AI cs.LGcited by 0

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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

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.

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References · 23

  1. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
  2. 2022Competition-Level Code Generation with AlphaCodecited by 1,462
  3. 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,391
  4. 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,310
  5. 2021CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generationcited by 906
  6. 2021CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generationcited by 744
  7. 2024A Survey on Large Language Models for Code Generationcited by 658
  8. 2021Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributionscited by 431
  9. 2023Can ChatGPT replace StackOverflow? A Study on Robustness and Reliability of Large Language Model Code Generationcited by 73
  10. 2023CodeScore: Evaluating Code Generation by Learning Code Executioncited by 67
  11. 2024Bugs in Large Language Models Generated Code: An Empirical Studycited by 60
  12. 2024What's Wrong with Your Code Generated by Large Language Models? An Extensive Studycited by 53
11 more
  1. 2024What Makes Large Language Models Reason in (Multi-Turn) Code Generation?cited by 29
  2. 2024An Empirical Study on Usage and Perceptions of LLMs in a Software Engineering Projectcited by 28
  3. 2024Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human Programmerscited by 27
  4. 2024The Impact of Large Language Models in Academia: from Writing to Speakingcited by 18
  5. 2024Distinguishing LLM-generated from Human-written Code by Contrastive Learningcited by 16
  6. 2024LiCoEval: Evaluating LLMs on License Compliance in Code Generationcited by 14
  7. 2024Does Your Neural Code Completion Model Use My Code? A Membership Inference Approachcited by 11
  8. 2024Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewritingcited by 11
  9. 2024Is This You, LLM? Recognizing AI-written Programs with Multilingual Code Stylometrycited by 6
  10. 2024AIGCodeSet: A New Annotated Dataset for AI Generated Code Detectioncited by 5
  11. 2025Detection of LLM-Paraphrased Code and Identification of the Responsible LLM Using Coding Style Featurescited by 4

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  1. 2025Are Large Language Models Robust in Understanding Code Against Semantics-Preserving Mutations?
  2. 2025A Study of LLMs' Preferences for Libraries and Programming Languages
  3. 2025On Code-Induced Reasoning in LLMs
  4. 2026Are you Synthesizing or Recalling? Evaluating LLMs on Algorithmic Code Retrieval
  5. 2024Assessing Code Generation with Intermediate Languages
  6. 2024Don't Transform the Code, Code the Transforms: Towards Precise Code Rewriting using LLMs
  7. 2026Subjective Code Preferences in Experts and Large Language Models ↗
  8. 2026Evaluating Language Models on Cross-Language Code Functional Equivalence