Project Description

Project aims to create tool for specific situations in which current cucumber testsuite used for Uyuni and SUSE Manager is too complex tool and, otherwise, in which manual testing is just still too much time consuming.

I would like to create tool, which quickly sets up all necessary stuff for area to be tested, so manual testing is limited to final tests and decision making if feature works or not.

This tool will be written in Rust language, because the language itself looks just cool (and has some very interesting concepts) and could be interesting choice for this purpose in combination of XMLRPC API provided by Uyuni/SUSE Manager as XMLRPC calls are very quick and handling of error states is easy.

Goal for this Hackweek

Implement following for retail features, so:

retail fomulas configuration
build hosts preparation
creation of kiwi image profiles
scheduling of kiwi image building
applying of highstate

...will be possible to test via this tool.

Setup of retail formulas will be handled via json files already used to store their configuration.

Resources

Join this project Leave this project

Looking for hackers with the skills:

uyuni retail xmlrpc rust testing

This project is part of:

Hack Week 20

Activity

over 4 years ago: ccalancha liked this project.

over 4 years ago: lkotek added keyword "uyuni" to this project.

over 4 years ago: lkotek added keyword "retail" to this project.

over 4 years ago: lkotek added keyword "xmlrpc" to this project.

over 4 years ago: lkotek added keyword "rust" to this project.

over 4 years ago: lkotek added keyword "testing" to this project.

over 4 years ago: lkotek started this project.

over 4 years ago: lkotek originated this project.

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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 (including bootstrapping with bootstrap script) and Salt-ssh clients

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):

Reposync (this will require using spacewalk-common-channels and adding channels to the .ini file)
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Bonus point: Java part for product identification, and monitoring enablement
Bonus point: sumaform enablement (https://github.com/uyuni-project/sumaform)
Bonus point: Documentation (https://github.com/uyuni-project/uyuni-docs)
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 :-)

In progress/done for Hack Week 25

Guide

We started writin a Guide: Adding a new client GNU Linux distribution to Uyuni at https://github.com/uyuni-project/uyuni/wiki/Guide:-Adding-a-new-client-GNU-Linux-distribution-to-Uyuni, to make things easier for everyone, specially those not too familiar wht Uyuni or not technical.

openSUSE Leap 16.0

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Curent Status We started last year, it's complete now for Hack Week 25! :-D

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Mail client with mailing list workflow support in Rust by acervesato

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Testing and adding GNU/Linux distributions on Uyuni by juliogonzalezgil

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

The idea is testing Salt (including bootstrapping with bootstrap script) and Salt-ssh clients

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):

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
Applying any basic salt state (including a formula)
Salt remote commands
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Bonus point: sumaform enablement (https://github.com/uyuni-project/sumaform)
Bonus point: Documentation (https://github.com/uyuni-project/uyuni-docs)
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
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In progress/done for Hack Week 25

Guide

openSUSE Leap 16.0

The distribution will all love!

https://en.opensuse.org/openSUSE:Roadmap#DRAFTScheduleforLeap16.0

Curent Status We started last year, it's complete now for Hack Week 25! :-D

[W] Reposync (this will require using spacewalk-common-channels and adding channels to the .ini file) NOTE: Done, client tools for SLMicro6 are using as those for SLE16.0/openSUSE Leap 16.0 are not available yet
[W] 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)
[W] Package management (install, remove, update...). Works, even reboot requirement detection

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 "graphical" image of the console or GUI 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 POC:

Main steps

Phase 1 - Plan
- study the available tools
- prepare a plan for the process to build
Phase 2 - Implement
- write and build a draft application
Phase 3 - Data
- 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
Phase 4 - Test
- 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.

Project references

Repository: openqa-needles-AI-driven