KubeVirt AI Contribution Policy
Overview
This policy establishes guidelines for contributions that involve Artificial Intelligence (AI) tools, including but not limited to Large Language Models (LLMs), code generation tools, and AI-assisted development environments. This is a living document that will evolve as AI technology and legal frameworks mature.
Motivation
AI tools are powerful assistants that can allow developers to become more productive when configured and used correctly.
This policy encourages their use within the KubeVirt project to boost both productivity and innovation while ensuring transparency. This allows the community to learn and refine our policies and practices accordingly in order to maximise the value of these tools.
Contributor Accountability
AI tools can produce verbose, over-engineered, or superficially-correct code that places a disproportionate review burden on maintainers. Disclosure creates accountability and helps ensure contributors take ownership of AI-assisted work. Contributors are expected to:
- Thoroughly review and understand every line of AI-generated code before submission
- Refine and groom AI output to meet project quality standards
- Take full ownership of all submitted content regardless of origin
Low-effort submissions that appear to be unreviewed AI output may be rejected without detailed feedback until properly refined. This applies to all contributions, but is particularly relevant for AI-assisted work.
Legal and Copyright Rationale
Disclosure also serves important legal purposes. Copyright law in this area continues to evolve, and as of current legal guidance, computer-generated work may not be considered an original work eligible for copyright protection in many jurisdictions. Additionally:
- AI training data may originate from materials with unclear or incompatible licenses
- Some AI tool vendors may retain rights to generated output, which could conflict with open source licensing
- Proper attribution helps maintain the integrity of the project's licensing under Apache 2.0
For further reading on these legal considerations, see the OpenInfra Foundation AI Policy and AI-Assisted Development and Open Source: Navigating Legal Issues.
AI Tool Disclosure Requirements
Disclosure
All contributors SHOULD disclose AI tool use when submitting code, documentation, or other content to the KubeVirt project.
Disclosure SHOULD take the form of a trailer line within the commit attributing the AI tool used. Acceptable formats include:
Assisted-by: Claude <noreply@anthropic.com>Co-authored-by: Claude <noreply@anthropic.com>Generated-by: Claude <noreply@anthropic.com>
Many AI coding tools automatically add Co-authored-by trailers—this is
acceptable and need not be changed to Assisted-by.
Authors MUST still adhere to KubeVirt's Developer's Certificate of Origin (DCO) requirements and sign off commits.
This will be aided through the use of the emerging
AGENT.md standard with symlinks provided to the
in project prompt configuration files of various agents. An example file will
be created within the kubevirt/.github
repository for projects to use as a base.
Scope of Disclosure
Disclosure is expected when AI tools have materially contributed to the submitted content.
Requires disclosure:
- AI wrote a function, class, or significant code block that you included
- AI suggested an algorithm, architecture, or approach you adopted
- AI generated tests, documentation, or commit messages you used
- AI-suggested solutions, refactoring, or significant debugging help that shaped the final implementation
Does not require disclosure:
- General Q&A or learning (even if it informed your approach)
- IDE autocomplete (Copilot line completions, IntelliSense)
- Using AI to explain existing code
- Asking AI to review your human-written code
- Spell checking or minor syntax corrections
- Content that has been substantially rewritten such that the original AI output is no longer recognizable
When in doubt, err on the side of disclosure—transparency benefits the community.
Acceptable Uses of AI Tools
AI tools are accepted as development assistants for:
- Code scaffolding: Generating boilerplate code and initial implementations
- Refactoring: Suggesting code improvements and modernization
- Testing: Creating test cases and test data
- Documentation: Drafting technical documentation and code comments
- Debugging: Identifying potential issues and suggesting fixes
- Research: Exploring architectural approaches and best practices
Contributor Responsibilities
Contributors are encouraged to leverage AI tools and are responsible to review and understand the content they are contributing. For code this must meet the existing coding standards for the project.
Community Perspectives on AI Contributions
Alternative Approaches
The KubeVirt community recognizes that projects have varying approaches to AI-generated contributions:
Restrictive Approach: Some projects, such as QEMU, have adopted policies to decline AI-generated contributions entirely. QEMU's position is based on:
- Uncertain copyright and licensing status of AI-generated content
- Potential conflicts with Developer's Certificate of Origin (DCO)
- Legal risks from training materials with restrictive licensing
Permissive Approach: Other projects, including those under the Linux Foundation umbrella, allow AI-generated contributions with proper disclosure and review.
KubeVirt has chosen a balanced, disclosure-based approach that emphasizes transparency, human oversight, and community review while leveraging AI tools' productivity benefits.
Legal and Licensing Considerations
Copyright Compliance
Contributors must ensure that:
- AI tool terms of service do not conflict with Apache 2.0 licensing
- No copyrighted material is inadvertently included in AI-generated output
- All third-party content is properly attributed and licensed
- The Developer's Certificate of Origin (DCO) can be legitimately signed
Employer Policies
Contributors should verify that their use of AI tools complies with their employer's policies regarding AI-generated code in open source contributions.
Review Process
Review Criteria
As with all contributions to the project reviewers should evaluate:
- Code quality and adherence to project standards
- Appropriate test coverage
- Security implications
- Long-term maintainability
Policy Evolution
This policy will be regularly reviewed and updated to reflect:
- Changes in AI technology capabilities
- Legal and regulatory developments
- Community feedback and experience
- Industry best practices
This policy could be eventually removed once these tools become standard development tools and the policy is superseded by other contribution requirements.
Questions and Clarifications
For questions about this policy, please:
- Open an issue in the community repository
- Discuss in the #kubevirt-dev Slack channel or kubevirt-dev@googlegroups.com mailing list
- Bring up during community meetings