mlsanityData to 14 Oct 2026

On Developers' Self-Declaration of AI-Generated Code: An Analysis of Practices

arXiv 2504.16485 ↗First posted 23 Apr 2025cs.SE cs.AIcited by 1

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Abstract (arXiv)

AI code generation tools have gained significant popularity among developers, who use them to assist in software development due to their capability to generate code. Existing studies mainly explored the quality, e.g., correctness and security, of AI-generated code, while in real-world software development, the prerequisite is to distinguish AI-generated code from human-written code, which emphasizes the need to explicitly declare AI-generated code by developers. To this end, this study intends to understand the ways developers use to self-declare AI-generated code and explore the reasons why developers choose to self-declare or not. We conducted a mixed-methods study consisting of two phases. In the first phase, we mined GitHub repositories and collected 613 instances of AI-generated code snippets. In the second phase, we conducted a follow-up practitioners' survey, which received 111 valid responses. Our research revealed the practices followed by developers to self-declare AI-generated code. Most practitioners (76.6%) always or sometimes self-declare AI-generated code. In contrast, other practitioners (23.4%) noted that they never self-declare AI-generated code. The reasons for self-declaring AI-generated code include the need to track and monitor the code for future review and debugging, and ethical considerations. The reasons for not self-declaring AI-generated code include extensive modifications to AI-generated code and the developers' perception that self-declaration is an unnecessary activity. We finally provided guidelines for practitioners to self-declare AI-generated code, addressing ethical and code quality concerns.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 26 of its 63 references that are in the map. First posted on arXiv 23 Apr 2025.

Builds on

26 of 63 references.

References · 26

  1. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  2. 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,391
  3. 2024A Survey on Large Language Models for Code Generationcited by 658
  4. 2021Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributionscited by 431
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  7. 2022Grounded Copilot: How Programmers Interact with Code-Generating Modelscited by 218
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14 more
  1. 2023Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPTcited by 71
  2. 2024Bugs in Large Language Models Generated Code: An Empirical Studycited by 60
  3. 2023No Need to Lift a Finger Anymore? Assessing the Quality of Code Generation by ChatGPTcited by 47
  4. 2023Assessing the Security of GitHub Copilot Generated Code -- A Targeted Replication Studycited by 26
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  6. 2024Distinguishing LLM-generated from Human-written Code by Contrastive Learningcited by 16
  7. 2024Rocks Coding, Not Development--A Human-Centric, Experimental Evaluation of LLM-Supported SE Taskscited by 13
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  11. 2022Automatically Recommend Code Updates: Are We There Yet?cited by 7
  12. 2023Discriminating Human-authored from ChatGPT-Generated Code Via Discernable Feature Analysiscited by 7
  13. 2024Is This You, LLM? Recognizing AI-written Programs with Multilingual Code Stylometrycited by 6
  14. 2024Tadashi: Enabling AI-Based Automated Code Generation With Guaranteed Correctnesscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Software Delegation Contracts: Measuring Reviewability in AI Coding-Agent Workcited by 0