Description
Overview
AI coding tools (LLMs, code assistants, AI agents) are rapidly becoming part of the developer workflow across the software industry. Open source communities are beginning to grapple with how these tools intersect with their development processes โ from code generation and review assistance to documentation, debugging, and large-scale refactoring. This microconference will bring together maintainers, developers, and tooling experts to discuss the practical realities, policies, risks, and opportunities of AI-assisted development in the open source ecosystem.
The goal is not to debate whether AI tools will be used โ developers are already using them โ but to align on how communities should adapt their processes, what guardrails are needed, and where these tools can deliver the most value with the least risk.
Example Subtopics
- AI-assisted code generation and review โ practical experiences, failure modes, and disclosure norms
- Large-scale refactoring with AI assistance, such as C-to-Rust conversions
- AI for debugging, crash analysis, and root cause identification
- Policy and process implications: attribution, copyright, licensing, and trust in AI-generated contributions
- AI-powered test case generation and fuzzing guidance
- Building project-aware AI tooling with domain-specific context and integration with existing development infrastructure
Key People Who Should Attend
- Major subsystem and project maintainers who are receiving AI-assisted contributions and need to make policy decisions
- Developers actively using AI tools in their open source workflows who can share real-world experiences
- AI tooling developers building tools targeted at open source and systems-level development
- Linux Foundation / legal experts for the policy, licensing, and attribution discussion
Previous Related Sessions
While there has not been a dedicated AI microconference at LPC before, related discussions have touched on adjacent topics in microconferences such as Kernel Testing & Dependability, Rust for Linux, and Toolchain. This would be the first session to bring together the AI-specific cross-cutting concerns that span all of these areas.
Expected Outcomes
- Community alignment on disclosure and attribution requirements for AI-assisted contributions
- Identification of high-value, low-risk use cases where AI tools should be encouraged
- Concrete next steps for building project-aware AI tooling
- Framework for evaluating AI-generated code in the review process
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Mr Yuan Tan (Nebula Security)
Kernel maintainers increasingly receive bug findings from fuzzers and AI-assisted tools. While these tools expand bug-finding coverage, they also create a growing triage burden. The quality of both bug reports and patches can vary substantially: findings may be false positives, lack a usable reproducer, duplicate existing issues, or come with patches that are incomplete or incorrect.โจ
We...
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Aleksandr Nogikh (Google)
[Syzbot][1] reports around 1,500 new fuzzer-detected findings in the Linux kernel each year. Even for straightforward bugs where the root cause is obvious, the manual effort of drafting, testing, and sending patches constitutes a significant effort on the kernel developers side.
To facilitate this process, we have launched an AI-assisted fix generation [workflow][2] in syzbot built around...
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Fuad Tabba (Google)
AI patch review is already here. Sashiko, the reviewer bot now running on many of the kernel's mailing lists, finds real bugs. On a hard subsystem it also invents them: an architectural detail that does not exist, reported confidently as a bug. Marc Zyngier described this on the list recently. An agent that gets "creative about specifications" is particularly annoying, he wrote, because the...
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Jarod Wilson (Red Hat)
The Problem: CVEs
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- CVEs: Common Vulnerabilities and Exposures
- February 2024, Linux kernel project becomes the Linux kernel CVE Numbering Authority (CNA)
- The number of Linux kernel CVEs skyrockets
- Red Hat customers expect CVE fixes/mitigations, with some having Service Level Agreements for delivery within X number of days, depending on severity
- Red Hat did not get an increase in... -
Andrea Righi (NVIDIA)
AI-assisted tooling is increasingly becoming part of Linux kernel development workflows, particularly for patch review and feedback generation. Sashiko has become the de facto standard for reviewing publicly posted patch series on mailing lists.
While Sashiko can also be used beyond the mailing-list workflow, its design is primarily optimized around upstream review interactions. This can...
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Mr Wander Costa (Red Hat)
Analyzing kernel crash dumps with drgn is powerful but demands fluency in drgn's Python API, kernel data structure layouts, and the right helper functions for each subsystem. drgn-mcp is an MCP (Model Context Protocol) server that exposes drgn's debugging capabilities as structured tools that AI assistants can call, enabling kernel developers to investigate crash dumps through natural language...
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