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Wednesday, August 29 • 2:10pm - 2:50pm
Building Stable Trees with Machine Learning - Sasha Levin, Microsoft & Julia Lawall, Inria

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Building stable trees is difficult; we are required to find only commits that fix bugs (needle) in the massive flow of commits that go upstream (haystack).

Currently the process is based on authors and maintainers tagging their commits properly and helping stable maintainers to know that they should be picking up these patches.

However, this doesn't always happen right. Commits get lost, forgotten, or never looked at to begin with. This means that important fixes are being left out of stable trees and not reaching the users who rely on stable trees for fixes.

This talk with go over a new approach to detect bug fixing commits in the kernel tree using machine learning, and demonstrate how it was used to submit over a thousand commits to various stable trees.

avatar for Julia Lawall

Julia Lawall

Senior Researcher, Inria
Julia Lawall is a Senior Research Scientist at Inria. Her research is at the intersection of programming languages and operating systems. She develops the tool Coccinelle and has over 2000 patches in the Linux kernel based on this work.

Sasha Levin

Kernel Engineer, NVIDIA
Sasha helps maintain the Linux Kernel Stable and LTS trees. He is currently employed by Google where he helps make Linux better. Previously, Sasha was employed by Microsoft and the Ksplice team in Oracle.

Wednesday August 29, 2018 2:10pm - 2:50pm PDT
Room 110