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Developer Perspectives on Licensing and Copyright Issues Arising from Generative AI for Software Development

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arxiv 2411.10877 v5 pith:JQGQ5DVP submitted 2024-11-16 cs.SE cs.AI

classification cs.SEcs.AI
keywords genaicodedeveloperscopyrighttheyviewsevolvinggenerative
verification ladder T0 review T1 audit T2 compute T3 formal
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Despite the utility that Generative AI (GenAI) tools provide for tasks such as writing code, the use of these tools raises important legal questions and potential risks, particularly those associated with copyright law. As lawmakers and regulators engage with those questions, the views of users can provide relevant perspectives. In this paper, we provide: (1) a survey of 574 developers on the licensing and copyright aspects of GenAI for coding, as well as follow-up interviews; (2) a snapshot of developers' views at a time when GenAI and perceptions of it are rapidly evolving; and (3) an analysis of developers' views, yielding insights and recommendations that can inform future regulatory decisions in this evolving field. Our results show the benefits developers derive from GenAI, how they view the use of AI-generated code as similar to using other existing code, the varied opinions they have on who should own or be compensated for such code, that they are concerned about data leakage via GenAI, and much more, providing organizations and policymakers with valuable insights into how the technology is being used and what concerns stakeholders would like to see addressed.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Exploring the Challenges and Opportunities of AI-assisted Codebase Generation

    cs.SE 2025-08 conditional novelty 6.0 of 10

    Developers prompting codebase-level AI assistants are often dissatisfied with generated code, citing missing functionality, poor code quality, and communication gaps, despite varied prompting strategies.

  2. CodeMirage: A Multi-Lingual Benchmark for Detecting AI-Generated and Paraphrased Source Code from Production-Level LLMs

    cs.SE 2025-05 conditional novelty 6.0 of 10

    CodeMirage is a ten-language, ten-LLM benchmark with original and paraphrased AI code, and it shows current AI-generated-code detectors drop sharply under cross-model and low-false-alarm settings.

  3. DevLicOps: A Framework for Mitigating Licensing Risks in AI-Generated Code

    cs.SE 2025-08 conditional novelty 4.0 of 10

    DevLicOps integrates license-compliance controls into the SDLC to reduce risk from AI-generated code, using policies, automated scans, manual audits, and indemnity-aware practices.

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