Q2Boot - A handy QEMU VM launcher by amanzini

Description

Q2Boot (Qemu Quick Boot) is a command-line tool that wraps QEMU to provide a streamlined experience for launching virtual machines. It automatically configures common settings like KVM acceleration, virtio drivers, and networking while allowing customization through both configuration files and command-line options.

The project originally was a personal utility in D, now recently rewritten in idiomatic Go. It lives at repository https://github.com/ilmanzo/q2boot

Goals

Improve the project, testing with different scenarios , address issues and propose new features. It will benefit of some basic integration testing by providing small sample disk images.

Resources

• https://github.com/ilmanzo/q2boot
• https://www.qemu.org/

---

SUSE KVM Best Practices - Focus on SAP Workloads and Use Cases by roseswe

Description

SUSE Best Practices around KVM, especially for SAP workloads. Early Google presentation already made from various customer projects and SUSE sources.

Goals

• Complete presentation we can reuse in SUSE Consulting projects
• 2025: Bring it to version 1.00 ready for customers

Resources

KVM (virt-manager) images

SUSE/SAP/KVM Best Practices

• https://documentation.suse.com/en-us/sles/15-SP6/single-html/SLES-virtualization/
• SAP Note 1522993 - "Linux: SAP on SUSE KVM - Kernel-based Virtual Machine" && 2284516 - SAP HANA virtualized on SUSE Linux Enterprise hypervisors https://me.sap.com/notes/2284516
• SUSECon24: [TUTORIAL-1253] Virtualizing SAP workloads with SUSE KVM || https://youtu.be/PTkpRVpX2PM
• SUSE Best Practices for SAP HANA on KVM - https://documentation.suse.com/sbp/sap-15/html/SBP-SLES4SAP-HANAonKVM-SLES15SP4/index.html

---

Create openSUSE images for Arm/RISC-V boards by avicenzi

Project Description

Create openSUSE images (or test generic EFI images) for Arm and/or RISC-V boards that are not yet supported.

Goal for this Hackweek

Create bootable images of Tumbleweed for SBCs that currently have no images available or are untested.

Consider generic EFI images where possible, as some boards can hold a bootloader.

Document in the openSUSE Wiki how to flash and use the image for a given board.

Hack Week 22

• NanoPC T4

Hack Week 21

• NanoPi M4B
• Radxa Zero

Resources

• https://en.opensuse.org/Portal:Arm
• https://en.opensuse.org/Portal:RISC-V

---

Create openSUSE images for Arm/RISC-V boards by avicenzi

Project Description

Create openSUSE images (or test generic EFI images) for Arm and/or RISC-V boards that are not yet supported.

Goal for this Hackweek

Create bootable images of Tumbleweed for SBCs that currently have no images available or are untested.

Consider generic EFI images where possible, as some boards can hold a bootloader.

Document in the openSUSE Wiki how to flash and use the image for a given board.

Hack Week 22

• NanoPC T4

Hack Week 21

• NanoPi M4B
• Radxa Zero

Resources

• https://en.opensuse.org/Portal:Arm
• https://en.opensuse.org/Portal:RISC-V

---

MCP Perl SDK by kraih

Description

We've been using the MCP Perl SDK to connect openQA with AI. And while the basics are working pretty well, the SDK is not fully spec compliant yet. So let's change that!

Goals

• Support for Resources
• All response types (Audio, Resource Links, Embedded Resources...)
• Tool/Prompt/Resource update notifications
• Dynamic Tool/Prompt/Resource lists
• New authentication mechanisms

Resources

• https://github.com/mojolicious/mojo-mcp
• https://modelcontextprotocol.io/specification/2025-06-18

---

openQA log viewer by mpagot

Description

*** Warning: Are You at Risk for VOMIT? ***

Do you find yourself staring at a screen, your eyes glossing over as thousands of lines of text scroll by? Do you feel a wave of text-based nausea when someone asks you to "just check the logs"?

You may be suffering from VOMIT (Verbose Output Mental Irritation Toxicity).

This dangerous, work-induced ailment is triggered by exposure to an overwhelming quantity of log data, especially from parallel systems. The human brain, not designed to mentally process 12 simultaneous autoinst-log.txt files, enters a state of toxic shock. It rejects the "Verbose Output," making it impossible to find the one critical error line buried in a 50,000-line sea of "INFO: doing a thing."

Before you're forced to rm -rf /var/log in a fit of desperation, we present the digital antacid.

No panic: we have The openQA Log Visualizer

This is the UI antidote for handling toxic log environments. It bravely dives into the chaotic, multi-machine mess of your openQA test runs, finds all the related, verbose logs, and force-feeds them into a parser.

image

Goals

Work on the existing POC openqa-log-visualizer about few specific tasks:

• add support for more type of logs
• extend the configuration file syntax beyond the actual one
• work on log parsing performance

Find some beta-tester and collect feedback and ideas about features

If time allow for it evaluate other UI frameworks and solutions (something more simple to distribute and run, maybe more low level to gain in performance).

Resources

openqa-log-visualizer

---

openQA tests needles elaboration using AI image recognition by mdati

Description

In the openQA test framework, to identify the status of a target SUT image, a screenshots of GUI or CLI-terminal images, the needles framework scans the many pictures in its repository, having associated a given set of tags (strings), selecting specific smaller parts of each available image. For the needles management actually we need to keep stored many screenshots, variants of GUI and CLI-terminal images, eachone accompanied by a dedicated set of data references (json).

A smarter framework, using image recognition based on AI or other image elaborations tools, nowadays widely available, could improve the matching process and hopefully reduce time and errors, during the images verification and detection process.

Goals

Main scope of this idea is to match a non-text status of a running openQA test, an image of a shell console or application-GUI screenshot, using less time and resources and with less errors in data preparation and use, than the actual openQA needles framework; that is:

• having a given SUT (system under test) GUI or CLI-terminal screenshot, with a local distribution of pixels or text commands related to a running test status,
• we want to identify a desired target, e.g. a screen image status or data/commands context,
  • based on AI/ML-pretrained archives containing object or other proper elaboration tools,
  • possibly able to identify also object not present in the archive, i.e. by means of AI/ML mechanisms.
• the matching result should be then adapted to continue working in the openQA test, likewise and in place of the same result that would have been produced by the original openQA needles framework.
• We expect an improvement of the matching-time(less time), reliability of the expected result(less error) and simplification of archive maintenance in adding/removing objects(smaller DB and less actions).

Hackweek step

POC:

• study the available tools
• prepare a plan for the process to build
• write and build a draft application
• prepare the data archive from a subset of needles
• initialize/pre-train the base archive
• select a screenshot from the subset, removing/changing some part
• run the POC application
• expect the image type is identified in a good %.

Resources

first step of this project is quite identification of useful resources for the scope; some possibilities are:

• SUSE AI and other ML tools (i.e. Tensorflow)
• Tools able to manage images
• RPA test tools (like i.e. Robot framework)
  
• other.

---