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Before contributing AI-generated code to a Linux project, review and understand the entire patch, verify the problem it claims to fix, test the change at a level that matches its impact, disclose meaningful tool assistance, and prepare the contribution for human review. For the Linux kernel, the submitter remains responsible for the code and its sign-off; kernel-specific guidance does not automatically apply to every other Linux project.
Start with the target project’s contribution rules
“Linux projects” do not all share one contribution process. The workflow below uses the Linux kernel because its project guidance addresses AI coding assistants, tool-generated content, testing, and patch submission directly. Before applying these steps elsewhere, read the target project’s own policy: its accepted submission method, disclosure expectations, testing requirements, and sign-off rules may differ.
For kernel work, begin with the kernel’s AI Coding Assistants guidance and its standard development-process documentation. If you are preparing an actual patch, also consult the notes for the relevant subsystem and maintainer tree. The general patch-submission guide says subsystem instructions may add requirements, and the subsystem maintainer’s tree may be a better base than mainline. Record the exact commit ID on which you based your work so you and reviewers can identify what was changed and against which code.
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Review the whole contribution before testing it
Do not treat the lines emitted by a coding assistant as the full unit of review. Inspect the complete resulting diff, including surrounding changes, removed code, generated files, and any edits you made after generation. The kernel’s Guidelines for Tool-Generated Content say: “You are expected to understand and to be able to defend everything you submit.” If you cannot explain the patch or respond meaningfully to questions about it, do not submit it yet.
Check behavior and neighboring code
Read the code around each change, not just the changed lines. Trace how the modified function is called and what it promises to callers. Check relevant API contracts, configuration dependencies, locking, error paths, and the intended behavior described by the patch. A change that looks locally reasonable may conflict with an assumption in neighboring code or handle only the success path while leaving an error path inconsistent.
Ask yourself what observable behavior should change and what should remain unchanged. For a bug fix, identify the conditions under which the failure occurs and how the patch alters those conditions. For a change that affects configuration-dependent code, establish which configurations include the modified path. These are practical review questions to help you satisfy the kernel’s requirement that you understand and defend the complete submission; they are not a substitute for project or subsystem-specific instructions.
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Review the patch for licensing compatibility and appropriate SPDX identifiers. The kernel’s tool-generated-content guidance places responsibility on the submitter to review generated code and ensure that the contribution is suitable to submit. Do not assume that because an assistant produced code, the assistant—or the tool’s provider—has taken responsibility for its correctness or licensing.
The human submitter must be able to stand behind the contribution. That includes understanding the complete change, addressing review comments, and adding their own sign-off when certifying the Developer’s Certificate of Origin. The Linux kernel’s AI Coding Assistants document is explicit: “AI agents MUST NOT add Signed-off-by tags.” The agent must not sign on a human’s behalf; the human contributor adds their own sign-off under the kernel process.
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Verify the claimed bug before accepting an AI-generated fix
If an assistant identifies a nontrivial bug, treat its report as a hypothesis to verify, not as evidence that the bug exists. The kernel AI guidance calls for attempting a reproducer. Try to make the failure happen under conditions relevant to the report, and check whether the observed behavior actually supports the stated diagnosis.
- Write down the claim. Identify the behavior said to be wrong, the conditions said to trigger it, and the expected behavior. Separate observations from the assistant’s explanation of why they happen.
- Attempt a reproducer. Exercise the reported conditions and see whether the failure occurs. Record what you actually observed. If you cannot reproduce it, distinguish that from proving that it cannot happen.
- Reassess the diagnosis. Compare the evidence with the proposed cause. If the report appears incorrect after verification, stop rather than submitting a patch for it.
- Check the fix against the evidence. Build the proposed change and use the reproducer, or a complete analysis when a reproducer is not available, to determine whether the fix addresses the claimed problem.
- Discard a fix that does not work. A patch that fails its reproducer or does not hold up under complete analysis is not ready to submit, even if it passes a style checker or appears well structured.
Be precise about uncertainty. If a reproducer was unavailable, say so. If a failure did not occur in your attempted test, do not describe the bug as reproduced. The kernel guidance asks contributors to verify the fix through a reproducer or complete analysis; it does not make an unverified assistant explanation equivalent to either.
Test the change in proportion to its behavior and risk
The AI-specific kernel guidance requires that a fix add no build warnings and pass checkpatch.pl. The wider kernel submission checklist describes additional forms of coverage, including relevant configuration builds, allnoconfig and allmodconfig, builds on multiple architectures, runtime tests with debugging and lockdep features, fault-injection checks, and testing against the latest linux-next tag. These are examples from the checklist, not a claim that every patch must run every test in every environment. Choose checks relevant to the change and available to you, and report accurately what you did not run.
Build and warning checks
Build the changed code in relevant configurations and inspect the result for warnings. Which configurations matter depends on where the change is compiled and how its behavior is selected. The kernel checklist’s examples include allnoconfig and allmodconfig; where appropriate, builds on multiple architectures can expose problems that a single build does not. Do not report a configuration or architecture as covered unless you actually built it.
Runtime, locking, and failure-path checks
When the changed behavior can be exercised at runtime, test the paths affected by the patch. The checklist points to runtime testing with debugging and lockdep features and to fault injection as possible checks. Their relevance depends on the code and the failure modes the patch touches. For example, a change whose correctness depends on locking warrants attention to the relevant locking behavior; a change to error handling may call for exercising the failure path rather than only the normal path. These are ways to select meaningful evidence, not universal kernel test mandates.
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Style and analysis tools
Run checkpatch.pl as a guide and review any findings rather than treating its output as an automatic verdict. Kernel documentation warns that the checker does not replace human judgment. Explain or justify remaining findings where appropriate, and do not confuse passing the checker with proving behavioral correctness.
The kernel materials also name tools such as sparse, smatch, and clang-tidy. Their mention does not establish that every tool applies to every patch or that a particular invocation is required. Use tools that are relevant to the code and the target project’s guidance, and describe their results only if you actually ran them. The submission checklist also includes a checkstack heuristic: a function using more than 512 bytes on the stack is a candidate for change. That is a checklist threshold for review attention, not a published statistic about defects or outcomes.
Choose and describe coverage honestly
A useful test record tells reviewers what was tested, under what relevant conditions, and what remains untested. If a build could not be completed, name that limitation rather than implying a successful build. If you lacked an architecture, configuration, runtime environment, or reproducer, make the gap explicit. A smaller but truthful test record is more useful than a broad claim that leaves the actual coverage unclear.
For a change with several independent effects, separate the evidence by effect: a style check does not establish that the failure is fixed, and a successful build does not establish that the runtime behavior is correct. Match each important claim in the patch description with the evidence that supports it. The kernel checklist’s coverage examples are prompts for deciding what matters; use engineering judgment and the subsystem’s instructions to determine what is feasible and proportionate.
Make the patch easy for maintainers to assess
A reviewable patch explains why a change is needed, what impact the problem has, how the proposed code addresses it, and what evidence supports the result. Keep logically distinct changes separate where possible so that each patch can be understood on its own. A reviewer should not have to infer the motivating problem from generated code or guess which tests were run.
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Use the appropriate base tree and identify the relevant subsystem ownership. The kernel submission guide recommends scripts/get_maintainer.pl to identify relevant maintainers and mailing lists. Follow the applicable maintainer-tree notes and recipient guidance when preparing the patch; the general kernel process normally sends patches inline by email with appropriate maintainers and lists copied.
When checkpatch.pl reports findings, consider them in context and explain remaining issues where needed. A clean checker result is useful evidence about the checks it performs, but it is not a substitute for a coherent patch description, a human review, or relevant behavioral testing.
Disclose meaningful AI assistance and sign off yourself
For advanced use of coding tools, the kernel patch guide calls for an Assisted-by attribution. The kernel’s tool-generated-content guidance also recommends explaining the tools used, their inputs, the portions they generated, relevant prompts or a summary of them, and how the patch was tested. Put that context in the cover letter or changelog where appropriate, following the instructions for the contribution. The guidance treats meaningful tool-generated content as in scope and advises transparency when uncertain.
Trivial typing, formatting, mechanical transformations, and similar menial uses are described as outside the scope of the tool-generated-content guidance. Even in those cases, disclosure can help reviewers understand the work. For substantive generated code, make the description useful rather than vague: identify the nature and extent of the assistance, what you reviewed or changed, and the verification actually performed. Do not imply that an AI agent added your sign-off; the human submitter reviews the code, takes responsibility, and signs off personally under the kernel’s process.
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Once the patch is prepared, send it through the project’s normal review channel with the relevant maintainers and lists included. For kernel patches, that normally means inline email. Be prepared to answer questions about the design, the generated portions, the tests, and any limitations you disclosed.
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When reviewers request changes, address the comments and explain what changed in later patch versions. Review is not complete merely because a patch was generated, built, or sent. Kernel maintainers retain discretion over how to handle tool-generated work: they may review it through the ordinary process, reject it, ask for extra scrutiny or testing, give it lower priority, or request additional explanation. The amount of generated content may affect the degree of scrutiny. Clear disclosure and a defensible test record make that discussion more useful, but do not guarantee acceptance.
Use these readiness questions before sending
The kernel documents do not define a formal score for AI-assisted patch readiness. The following questions synthesize the guidance into a final review; they are a practical decision aid, not an official kernel rubric.
| Readiness area | Questions to answer |
|---|---|
| Human understanding | Can you explain every changed part and defend the complete patch, including its licensing and relevant behavior? |
| Bug evidence | Is there a reproducer or a complete analysis supporting the claimed problem and the proposed fix? |
| Verification | Does the fix work against the reproducer or analysis, and does the change build without warnings? |
| Coverage | Have you considered relevant configurations, architectures, runtime paths, and failure conditions? Have you stated what could not be tested? |
| Transparency | Have you described meaningful tool involvement and the generated portions with appropriate attribution? |
| Reviewability | Are the problem, impact, solution, test evidence, base tree, and maintainer routing clear? |
If you cannot answer a question that is material to the change, resolve the gap or state it plainly before submission. The purpose is not to make every patch undergo identical testing; it is to make the evidence, responsibility, and remaining uncertainty visible to the people reviewing it.
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This workflow reflects the Linux kernel’s AI Coding Assistants guidance, Guidelines for Tool-Generated Content, patch-submission guide, and submission checklist as accessed on October 13, 2026. These are official kernel project documents, and their revisions can change. Recheck the current versions and the relevant subsystem’s instructions when preparing a contribution. Their requirements are kernel-specific; another Linux project may set different rules for tool disclosure, sign-off, testing, or submission.
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