7. AI tool policy

7.1. Motivation

As a community ns-3 values contributions from new as well as experienced developers, as discussed in our [Code of Conduct Policy](https://www.nsnam.org/about/governance/policies/). We expect that participating in ns-3 will be a learning experience, especially for new contributors, and we strive to support that learning throughout our code review process.

As a project we rely on various external tools to support development, and encourage developers to use whatever tools improve their productivity and quality of contributions, including AI-type tools. There is a downside, however, in over-reliance on AI tools, when contributors don’t fully understand what the tool has generated, haven’t reviewed it fully themselves, and are not committed to learning themselves.

As a project we recognize that code reviews are not free; they _do_ consume reviewers’ and maintainers’ time and effort. Therefore we strive to find a balance between supporting new contributors and being judicious in committing reviewers time.

We believe a key factor in achieving that balance is to recognize that participation in ns-3 is fundamentally a _human_ process. Therefore we value interacting with human contributors to enhance submissions, enabling contributor learning, and maintaining high quality ns-3 code. When contributors rely excessively on AI tools they put ns-3 reviewers in the position of refining the AI output, through the mediation of the submitter, rather than working directly with an engaged contributor. This does not contribute to learning, by contributors or maintainers, nor to efficient enhancement of ns-3.

Therefore the project has adopted this policy guiding use of AI tools.

This policy is mostly copied from a proposal being discussed for LLVM.

7.1.1. Summary of the policy

The main principles of this policy are * Contributors can use whatever tools they would like * There must be a human in the loop * Contributors must read and review all generated contributions _before_ asking for review * Contributions containing substantial generated content should be labeled * Agents must not take action in our digital spaces without human approval * Issues labeled “good first issue” are strictly for humans; AI tools must not be used

7.2. Policy

ns-3’s policy is that contributors can use whatever tools they would like to craft their contributions, but there must be a human in the loop. Contributors must read and review all LLM-generated code or text before they ask other project members to review it. The contributor is always the author and is fully accountable for their contributions. Contributors should be sufficiently confident that the contribution is high enough quality that asking for a review is a good use of scarce maintainer time, and they should be able to answer questions about their work during review.

We expect that new contributors will be less confident in their contributions, and our guidance to them is to start with small contributions that they can fully understand to build confidence. We aspire to be a welcoming community that helps new contributors grow their expertise, but learning involves taking small steps, getting feedback, and iterating. Passing maintainer feedback to an LLM doesn’t help anyone grow, and does not sustain our community.

Contributors are expected to be transparent and label contributions that contain substantial amounts of tool-generated content. Our policy on labelling is intended to facilitate reviews, and not to track which parts of ns-3 are generated. Contributors should note tool usage in their merge request description, commit message, or wherever authorship is normally indicated for the work. For instance, use a commit message trailer like Assisted-by: (name and version of code assistant). This transparency helps the community develop best practices and understand the role of these new tools.

This policy includes, but is not limited to, the following kinds of contributions:

  • Code, usually in the form of a merge request

  • RFCs or design proposals

  • Issues or security vulnerabilities

  • Comments and feedback on merge requests

7.3. Details

To ensure sufficient self review and understanding of the work, it is strongly recommended that contributors write MR descriptions themselves (if needed, using tools for translation or copy-editing). The description should explain the motivation, implementation approach, expected impact, and any open questions or uncertainties to the same extent as a contribution made without tool assistance.

An important implication of this policy is that it bans agents that take action in our digital spaces without human approval, such as the GitHub @claude agent. Automated review tools that may publish review comments without human review can be considered by the ns-3 project as a possible exception to this policy on a case-by-case basis. However, an opt-in review tool that keeps a human in the loop is acceptable under this policy. As another example, using an LLM to generate documentation, which a contributor manually reviews for correctness, edits, and then posts as a MR, is an approved use of tools under this policy.

AI tools must not be used to fix GitLab issues labelled good first issue. These issues are generally not urgent, and are intended to be learning opportunities for new contributors to get familiar with the codebase. Whether you are a newcomer or not, fully automating the process of fixing this issue squanders the learning opportunity and doesn’t add much value to the project. Using AI tools to fix issues labelled as “good first issues” is forbidden.

7.4. Extractive Contributions

The reason for our human in the loop contribution policy is that processing patches, MRs, RFCs, and comments to ns-3 is not free – it takes a lot of maintainer time and energy to review those contributions! Sending the unreviewed output of an LLM to open source project maintainers extracts work from them in the form of design and code review, so we call this kind of contribution an extractive contribution.

Prior to the advent of LLMs, open source project maintainers would often review any and all changes sent to the project simply because posting a change for review was a sign of interest from a potential long-term contributor. While new tools enable more development, it shifts effort from the implementer to the reviewer, and our policy exists to ensure that we value and do not squander maintainer time.

Reviewing changes from new contributors is part of growing the next generation of contributors and sustaining the project. We want the ns-3 project to be welcoming and open to aspiring engineers who are willing to invest time and effort to learn and grow, because growing our contributor base and recruiting new maintainers helps sustain the project over the long term.

7.5. Handling Violations

If a maintainer judges that a contribution doesn’t comply with this policy, they should paste the following response to request changes:

This MR doesn't appear to comply with our [our policy on tool-generated
content](https://www.nsnam.org/docs/contributing/html/general.html#ai-policy),
and requires additional justification for why it is valuable enough to the
project for us to review it. Please see our developer policy on
AI-generated contributions: (URL TBD)

The best ways to make a change less extractive and more valuable are to reduce its size or complexity or to increase its usefulness to the community. These factors are impossible to weigh objectively, and our project policy leaves this determination up to the maintainers of the project, i.e. those who are doing the work of sustaining the project.

If or when it becomes clear that a GitLab issue or MR is off-track and not moving in the right direction, maintainers should apply the extractive label to help other reviewers prioritize their review time. If the contributor doesn’t take steps to fix the reported issues within a short time (days to few weeks), the MR may be closed.

7.7. Examples of when to mention tool use

We expect that LLM-based tools will increasingly be integrated into IDEs and other developer tools, and it will be difficult to determine when to mention tool use in a MR description, header file of a contribution, or commit message. We suggest the following broad guidelines:

  • if the LLM provided inputs along the lines of cleanup, linting, catching of small mistakes, debugging of compiler error messages, etc., use does not need to be mentioned or disclosed. Use of LLMs to auto-generate Doxygen of class methods or member variables need not be mentioned explicitly.

  • if the LLM generated a substantial chunk of original code (implementation or tests), or documentation (such as Sphinx), use should be disclosed

  • if the LLM was used to port another implementation to ns-3, use should be disclosed

When in doubt, ask yourself if you asked the LLM to generate something new for you, or if you instead asked it to review and tidy up something that you generated largely by yourself. If the former, disclose the tool use, but otherwise, use need not be mentioned.

Here are some examples of commit messages that demonstrate how to apply the principles of this policy:

core: Create mp-units attribute wrappers
Claude Code assisted with implementation

Other examples will be provided at a later date.

7.8. Use of LLMs to review merge requests

LLMs may be used to review merge requests by other contributors, but reviewers should not blindly copy comments generated by the LLM without human review, and the submitter must have high confidence that the LLM review comment is technically correct. Again, the principle of human in the loop should apply.