WEB_PAGES:

Introduction:

As a qa-automation tester in Product QA for SLES and SUSE-Manager, the SUTs I test (system under test) (like SLE-12-SP2-beta-etc) are changing every day (new packages, patches are merged to SP2, files changes and so on).

Problem: we don't have a tool that give us metadata about the system, like machinery well do.

machinery inspect SUT machinery show SUT

Problem : what changed from system SLE12-SP-BUILD 8000 from to 8400 ? ( oh, i lost the mail from release manager ! )

machinery compare Problem : i found a regression with systemd-tests-suite on SLEnkins: the testsuite fail on BUILD 7400 , but build 7399 is still OK.

what exactly has changed for the package, but also for the system? -> Machinery

Problem: As QA i found a BUG on NFS. I have to report a bug.

Machinery can help me to fill the bug, giving me exact information about really different systems (SLES-12-SP1, openSUSE), etc, what has changed with NFS ? Or Fedora side?

RESULTS

First i want to thank the machinery team, especially Mauro and Manuel that supported me. On this hackweek, have integrated machinery for qa-automation on the library https://github.com/okirch/susetest, and in the SLEnkins automation Framwork.

This work really nice, for scanning systems under test. (SLES, openSUSE)

For qa-automation, machinery works nice and i achieved what i was expecting ! :)

I can scan, compare systems. This could be a FEDORA, DEBIAN, ArchLinux whatever against a openSUSE or a SLES.

In QA and Development, and even Relase Management Perspective this is awesome.

NEW HACK ! :

Revolutionar Perspective for QAAUTOTESTING with Machinery

I'm really glad, i can show you this :

https://slenkins.suse.de/jenkins/job/suite-machinery/32/console

In this example, i compare a SLES-12-SP2-LATEST, with 3-4 builds before.

RESULTS is amazing

With machinery i achieved to compare differents builds from SLES-12-SP2, thanks to the scope, i can see exactly was has changed and was not. I can compare a SLE_12-SP2-GNOME with a SLE-12-SP2-Default, and tracks perfectly changes.

Concrete examples are here :

Scan of a system With console log for machinery ( after the tests are executed) https://slenkins.suse.de/jenkins/view/Test%20suites/job/suite-machinery/13/console

Or with the inspect command redirect to a file.txt to workspace jenkins:

``` setup() machinery_sut = machinery(sut)

try: sometest(sut) machinerysut.inspect() machinerysut.show("tests-machinery") machinerysut.compare("SLE-12-SP2-BUILDXXX-GNOME") ```

Looking for hackers with the skills:

qa-automation susetest python slenkins machinery

This project is part of:

Hack Week 14

Activity

  • over 8 years ago: okurz liked this project.
  • over 8 years ago: dmaiocchi added keyword "machinery" to this project.
  • over 8 years ago: dmaiocchi added keyword "slenkins" to this project.
  • over 8 years ago: dmaiocchi added keyword "qa-automation" to this project.
  • over 8 years ago: dmaiocchi added keyword "susetest" to this project.
  • over 8 years ago: dmaiocchi added keyword "python" to this project.
  • over 8 years ago: mamorales liked this project.
  • over 8 years ago: evshmarnev liked this project.
  • over 8 years ago: e_bischoff liked this project.
  • over 8 years ago: dmaiocchi started this project.
  • over 8 years ago: dmaiocchi originated this project.

  • Comments

    • e_bischoff
      over 8 years ago by e_bischoff | Reply

      For point 2), snapshots would be an alternative. Which does not mean that using machinery to do that is not interesting - on the contrary!

    • dmaiocchi
      over 8 years ago by dmaiocchi | Reply

      ok first result are avaible here: https://slenkins.suse.de/jenkins/view/Test%20suites/job/suite-machinery/10/console

    Similar Projects

    Make more sense of openQA test results using AI by livdywan

    Description

    AI has the potential to help with something many of us spend a lot of time doing which is making sense of openQA logs when a job fails.

    User Story

    Allison Average has a puzzled look on their face while staring at log files that seem to make little sense. Is this a known issue, something completely new or maybe related to infrastructure changes?

    Goals

    • Leverage a chat interface to help Allison
    • Create a model from scratch based on data from openQA
    • Proof of concept for automated analysis of openQA test results

    Bonus

    • Use AI to suggest solutions to merge conflicts
      • This would need a merge conflict editor that can suggest solving the conflict
    • Use image recognition for needles

    Resources

    Timeline

    Day 1

    • Conversing with open-webui to teach me how to create a model based on openQA test results

    Day 2

    Highlights

    • I briefly tested compared models to see if they would make me more productive. Between llama, gemma and mistral there was no amazing difference in the results for my case.
    • Convincing the chat interface to produce code specific to my use case required very explicit instructions.
    • Asking for advice on how to use open-webui itself better was frustratingly unfruitful both in trivial and more advanced regards.
    • Documentation on source materials used by LLM's and tools for this purpose seems virtually non-existent - specifically if a logo can be generated based on particular licenses

    Outcomes

    • Chat interface-supported development is providing good starting points and open-webui being open source is more flexible than Gemini. Although currently some fancy features such as grounding and generated podcasts are missing.
    • Allison still has to be very experienced with openQA to use a chat interface for test review. Publicly available system prompts would make that easier, though.


    ClusterOps - Easily install and manage your personal kubernetes cluster by andreabenini

    Description

    ClusterOps is a Kubernetes installer and operator designed to streamline the initial configuration and ongoing maintenance of kubernetes clusters. The focus of this project is primarily on personal or local installations. However, the goal is to expand its use to encompass all installations of Kubernetes for local development purposes.
    It simplifies cluster management by automating tasks and providing just one user-friendly YAML-based configuration config.yml.

    Overview

    • Simplified Configuration: Define your desired cluster state in a simple YAML file, and ClusterOps will handle the rest.
    • Automated Setup: Automates initial cluster configuration, including network settings, storage provisioning, special requirements (for example GPUs) and essential components installation.
    • Ongoing Maintenance: Performs routine maintenance tasks such as upgrades, security updates, and resource monitoring.
    • Extensibility: Easily extend functionality with custom plugins and configurations.
    • Self-Healing: Detects and recovers from common cluster issues, ensuring stability, idempotence and reliability. Same operation can be performed multiple times without changing the result.
    • Discreet: It works only on what it knows, if you are manually configuring parts of your kubernetes and this configuration does not interfere with it you can happily continue to work on several parts and use this tool only for what is needed.

    Features

    • distribution and engine independence. Install your favorite kubernetes engine with your package manager, execute one script and you'll have a complete working environment at your disposal.
    • Basic config approach. One single config.yml file with configuration requirements (add/remove features): human readable, plain and simple. All fancy configs managed automatically (ingress, balancers, services, proxy, ...).
    • Local Builtin ContainerHub. The default installation provides a fully configured ContainerHub available locally along with the kubernetes installation. This configuration allows the user to build, upload and deploy custom container images as they were provided from external sources. Internet public sources are still available but local development can be kept in this localhost server. Builtin ClusterOps operator will be fetched from this ContainerHub registry too.
    • Kubernetes official dashboard installed as a plugin, others planned too (k9s for example).
    • Kubevirt plugin installed and properly configured. Unleash the power of classic virtualization (KVM+QEMU) on top of Kubernetes and manage your entire system from there, libvirtd and virsh libs are required.
    • One operator to rule them all. The installation script configures your machine automatically during installation and adds one kubernetes operator to manage your local cluster. From there the operator takes care of the cluster on your behalf.
    • Clean installation and removal. Just test it, when you are done just use the same program to uninstall everything without leaving configs (or pods) behind.

    Planned features (Wishlist / TODOs)

    • Containerized Data Importer (CDI). Persistent storage management add-on for Kubernetes to provide a declarative way of building and importing Virtual Machine Disks on PVCs for


    SUSE AI Meets the Game Board by moio

    Use tabletopgames.ai’s open source TAG and PyTAG frameworks to apply Statistical Forward Planning and Deep Reinforcement Learning to two board games of our own design. On an all-green, all-open source, all-AWS stack!
    A chameleon playing chess in a train car, as a metaphor of SUSE AI applied to games


    AI + Board Games

    Board games have long been fertile ground for AI innovation, pushing the boundaries of capabilities such as strategy, adaptability, and real-time decision-making - from Deep Blue's chess mastery to AlphaZero’s domination of Go. Games aren’t just fun: they’re complex, dynamic problems that often mirror real-world challenges, making them interesting from an engineering perspective.

    As avid board gamers, aspiring board game designers, and engineers with careers in open source infrastructure, we’re excited to dive into the latest AI techniques first-hand.

    Our goal is to develop an all-open-source, all-green AWS-based stack powered by some serious hardware to drive our board game experiments forward!


    Project Goals

    1. Set Up the Stack:

      • Install and configure the TAG and PyTAG frameworks on SUSE Linux Enterprise Base Container Images.
      • Integrate with the SUSE AI stack for GPU-accelerated training on AWS.
      • Validate a sample GPU-accelerated PyTAG workload on SUSE AI.
      • Ensure the setup is entirely repeatable with Terraform and configuration scripts, documenting results along the way.
    2. Design and Implement AI Agents:

      • Develop AI agents for the two board games, incorporating Statistical Forward Planning and Deep Reinforcement Learning techniques.
      • Fine-tune model parameters to optimize game-playing performance.
      • Document the advantages and limitations of each technique.
    3. Test, Analyze, and Refine:

      • Conduct AI vs. AI and AI vs. human matches to evaluate agent strategies and performance.
      • Record insights, document learning outcomes, and refine models based on real-world gameplay.

    Technical Stack

    • Frameworks: TAG and PyTAG for AI agent development
    • Platform: SUSE AI
    • Tools: AWS for high-performance GPU acceleration

    Why This Project Matters

    This project not only deepens our understanding of AI techniques by doing but also showcases the power and flexibility of SUSE’s open-source infrastructure for supporting high-level AI projects. By building on an all-open-source stack, we aim to create a pathway for other developers and AI enthusiasts to explore, experiment, and deploy their own innovative projects within the open-source space.


    Our Motivation

    We believe hands-on experimentation is the best teacher.

    Combining our engineering backgrounds with our passion for board games, we’ll explore AI in a way that’s both challenging and creatively rewarding. Our ultimate goal? To hack an AI agent that’s as strategic and adaptable as a real human opponent (if not better!) — and to leverage it to design even better games... for humans to play!


    Testing and adding GNU/Linux distributions on Uyuni by juliogonzalezgil

    Join the Gitter channel! https://gitter.im/uyuni-project/hackweek

    Uyuni is a configuration and infrastructure management tool that saves you time and headaches when you have to manage and update tens, hundreds or even thousands of machines. It also manages configuration, can run audits, build image containers, monitor and much more!

    Currently there are a few distributions that are completely untested on Uyuni or SUSE Manager (AFAIK) or just not tested since a long time, and could be interesting knowing how hard would be working with them and, if possible, fix whatever is broken.

    For newcomers, the easiest distributions are those based on DEB or RPM packages. Distributions with other package formats are doable, but will require adapting the Python and Java code to be able to sync and analyze such packages (and if salt does not support those packages, it will need changes as well). So if you want a distribution with other packages, make sure you are comfortable handling such changes.

    No developer experience? No worries! We had non-developers contributors in the past, and we are ready to help as long as you are willing to learn. If you don't want to code at all, you can also help us preparing the documentation after someone else has the initial code ready, or you could also help with testing :-)

    The idea is testing Salt and Salt-ssh clients, but NOT traditional clients, which are deprecated.

    To consider that a distribution has basic support, we should cover at least (points 3-6 are to be tested for both salt minions and salt ssh minions):

    1. Reposync (this will require using spacewalk-common-channels and adding channels to the .ini file)
    2. Onboarding (salt minion from UI, salt minion from bootstrap scritp, and salt-ssh minion) (this will probably require adding OS to the bootstrap repository creator)
    3. Package management (install, remove, update...)
    4. Patching
    5. Applying any basic salt state (including a formula)
    6. Salt remote commands
    7. Bonus point: Java part for product identification, and monitoring enablement
    8. Bonus point: sumaform enablement (https://github.com/uyuni-project/sumaform)
    9. Bonus point: Documentation (https://github.com/uyuni-project/uyuni-docs)
    10. Bonus point: testsuite enablement (https://github.com/uyuni-project/uyuni/tree/master/testsuite)

    If something is breaking: we can try to fix it, but the main idea is research how supported it is right now. Beyond that it's up to each project member how much to hack :-)

    • If you don't have knowledge about some of the steps: ask the team
    • If you still don't know what to do: switch to another distribution and keep testing.

    This card is for EVERYONE, not just developers. Seriously! We had people from other teams helping that were not developers, and added support for Debian and new SUSE Linux Enterprise and openSUSE Leap versions :-)

    Pending

    FUSS

    FUSS is a complete GNU/Linux solution (server, client and desktop/standalone) based on Debian for managing an educational network.

    https://fuss.bz.it/

    Seems to be a Debian 12 derivative, so adding it could be quite easy.

    • [ ] Reposync (this will require using spacewalk-common-channels and adding channels to the .ini file)
    • [ ] Onboarding (salt minion from UI, salt minion from bootstrap scritp, and salt-ssh minion) (this will probably require adding OS to the bootstrap repository creator)
    • [ ] Package management (install, remove, update...)
    • [ ] Patching (if patch information is available, could require writing some code to parse it, but IIRC we have support for Ubuntu already)
    • [ ] Applying any basic salt state (including a formula)
    • [ ] Salt remote commands
    • [ ] Bonus point: Java part for product identification, and monitoring enablement


    Symbol Relations by hli

    Description

    There are tools to build function call graphs based on parsing source code, for example, cscope.

    This project aims to achieve a similar goal by directly parsing the disasembly (i.e. objdump) of a compiled binary. The assembly code is what the CPU sees, therefore more "direct". This may be useful in certain scenarios, such as gdb/crash debugging.

    Detailed description and Demos can be found in the README file:

    Supports x86 for now (because my customers only use x86 machines), but support for other architectures can be added easily.

    Tested with python3.6

    Goals

    Any comments are welcome.

    Resources

    https://github.com/lhb-cafe/SymbolRelations

    symrellib.py: mplements the symbol relation graph and the disassembly parser

    symrel_tracer*.py: implements tracing (-t option)

    symrel.py: "cli parser"


    SUSE Prague claw machine by anstalker

    Project Description

    The idea is to build a claw machine similar to e.g. this one:

    example image

    Why? Well, it could be a lot of fun!

    But also it's a great way to dispense SUSE and openSUSE merch like little Geekos at events like conferences, career fairs and open house events.

    Goal for this Hackweek

    Build an arcade claw machine.

    Resources

    In French, an article about why you always lose in claw machine games:

    We're looking for handy/crafty people in the Prague office:

    • woodworking XP or equipment
    • arduino/raspi embedded programming knowledge
    • Anthony can find a budget for going to GM and buying servos and such ;)