Automated Test Report reviewer by oscar-barrios

Description

In SUMA/Uyuni team we spend a lot of time reviewing test reports, analyzing each of the test cases failing, checking if the test is a flaky test, checking logs, etc.

Goals

Speed up the review by automating some parts through AI, in a way that we can consume some summary of that report that could be meaningful for the reviewer.

Resources

No idea about the resources yet, but we will make use of:

• HTML/JSON Report (text + screenshots)
• The Test Suite Status GithHub board (via API)
• The environment tested (via SSH)
• The test framework code (via files)

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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

• TensorFlow
• open-webui
• openQA instance of openSUSE
• openQA documentation

Timeline

Day 1

• Conversing with open-webui to teach me how to create a model based on openQA test results
  • Asking for example code using TensorFlow in Python
  • Discussing log files to explore what to analyze
  • Drafting a new project called Testimony (based on Implementing a containerized Python action) - the project name was also suggested by the assistant

Day 2

• Using NotebookLLM (Gemini) to produce conversational versions of blog posts
  • Writing openQA tests in Python
  • Developing in distrobox containers
• Researching the possibility of creating a project logo with AI
  • Asking open-webui, persons with prior experience and conducting a web search for advice

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.

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Drag Race - comparative performance testing for pull requests by balanza

Description

«Sophia, a backend developer, submitted a pull request with optimizations for a critical database query. Once she pushed her code, an automated load test ran, comparing her query against the main branch. Moments later, she saw a new comment automatically added to her PR: the comparison results showed reduced execution time and improved efficiency. Smiling, Sophia messaged her team, “Performance gains confirmed!”»

Goals

• To have a convenient and ergonomic framework to describe test scenarios, including environment and seed;
• to compare results from different tests
• to have a GitHub action that executes such tests on a CI environment

Resources

The MVP will be built on top of Preevy and K6.

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Hack on isotest-ng - a rust port of isotovideo (os-autoinst aka testrunner of openQA) by szarate

Description

Some time ago, I managed to convince ByteOtter to hack something that resembles isotovideo but in Rust, not because I believe that Perl is dead, but more because there are certain limitations in the perl code (how it was written), and its always hard to add new functionalities when they are about implementing a new backend, or fixing bugs (Along with people complaining that Perl is dead, and that they don't like it)

In reality, I wanted to see if this could be done, and ByteOtter proved that it could be, while doing an amazing job at hacking a vnc console, and helping me understand better what RuPerl needs to work.

I plan to keep working on this for the next few years, and while I don't aim for feature completion or replacing isotovideo tih isotest-ng (name in progress), I do plan to be able to use it on a daily basis, using specialized tooling with interfaces, instead of reimplementing everything in the backend

Todo

• Add make targets for testability, e.g "spawn qemu and type"
• Add image search matching algorithm
• Add a Null test distribution provider
• Add a Perl Test Distribution Provider
• Fix unittests https://github.com/os-autoinst/isotest-ng/issues/5
• Research OpenTofu how to add new hypervisors/baremetal to OpenTofu
• Add an interface to openQA cli

Goals

• Implement at least one of the above, prepare proposals for GSoC
• Boot a system via it's BMC

Resources

See https://github.com/os-autoinst/isotest-ng

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Yearly Quality Engineering Ask me Anything - AMA for not-engineering by szarate

Goal

Get a closer look at how developers work on the Engineering team (R & D) of SUSE, and close the collaboration gap between GSI and Engineering

Why?

Santiago can go over different development workflows, and can do a deepdive into how Quality Engineering works (think of my QE Team, the advocates for your customers), The idea of this session is to help open the doors to opportunities for collaboration, and broaden our understanding of SUSE as a whole.

Objectives

• Give $audience a small window on how to get some questions answered either on the spot or within days of how some things at engineering are done
• Give Santiago Zarate from Quality Engineering a look into how $audience sees the engineering departments, and find out possibilities of further collaboration

How?

By running an "Ask me Anything" session, which is a format of a kind of open Q & A session, where participants ask the host multiple questions.

How to make it happen?

I'm happy to help joining a call or we can do it async (online/in person is more fun). Ping me over email-slack and lets make the magic happen!. Doesn't need to be during hackweek, but we gotta kickstart the idea during hackweek ;)

Rules

The rules are simple, the more questions the more fun it will be; while this will be only a window into engineering, it can also be the place to help all of us get to a similar level of understanding of the processes that are behind our respective areas of the organization.

Dynamics

The host will be monitoring the questions on some pre-agreed page, and try to answer to the best of their knowledge, if a question is too difficult or the host doesn't have the answer, he will do his best to provide an answer at a later date.

Atendees are encouraged to add questions beforehand; in the case there aren't any, we would be looking at how Quality Engineering tests new products or performs regression tests

Agenda

• Introduction of Santiago Zarate, Product Owner of Quality Engineering Core team
• Introduction of the Group/Team/Persons interested
• Ice breaker
• AMA time! Add your questions $PAGE
• Looking at QE Workflows: How is
  • A maintenance update being tested before being released to our customers
  • Products in development are tested before making it generally available
• Engineering Opportunity Board

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Cobbler Angular Web Interface by SchoolGuy

Project Description

The old Cobbler webinterface was built into the server, leading to a huge dependency stack only required for a few people.

Goal for this Hackweek

The project should aim to finalize the first prototype of the new Angular based web interface.

A secondary goal of this hackweek is to learn a lot of Angular.

Update for Hackweek 24

The GH project received some traction since I have some vacation. As such it is my aim to get a first alpha released to close the milestone 0.0.1 (or whatever version I can release with semantic release).

Resources

• GitHub Repo
• GitHub Project

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Ansible for add-on management by lmanfredi

Description

Machines can contains various combinations of add-ons and are often modified during the time.

The list of repos can change so I would like to create an automation able to reset the status to a given state, based on metadata available for these machines

Goals

Create an Ansible automation able to take care of add-on (repo list) configuration using metadata as reference

Resources

• Machines
• Repositories
• Developing modules
• Basic VM Guest management
• Module zypper_repository_list
• ansible-collections community.general
  • plugins/modules/zypper.py
  • plugins/modules/zypper_repository.py
  • plugins/modules/zypperrepositoryinfo.py

Results

Created WIP project Ansible-add-on-openSUSE

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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

• TensorFlow
• open-webui
• openQA instance of openSUSE
• openQA documentation

Timeline

Day 1

• Conversing with open-webui to teach me how to create a model based on openQA test results
  • Asking for example code using TensorFlow in Python
  • Discussing log files to explore what to analyze
  • Drafting a new project called Testimony (based on Implementing a containerized Python action) - the project name was also suggested by the assistant

Day 2

• Using NotebookLLM (Gemini) to produce conversational versions of blog posts
  • Writing openQA tests in Python
  • Developing in distrobox containers
• Researching the possibility of creating a project logo with AI
  • Asking open-webui, persons with prior experience and conducting a web search for advice

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.

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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"

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Saline (state deployment control and monitoring tool for SUSE Manager/Uyuni) by vizhestkov

Project Description

Saline is an addition for salt used in SUSE Manager/Uyuni aimed to provide better control and visibility for states deploymend in the large scale environments.

In current state the published version can be used only as a Prometheus exporter and missing some of the key features implemented in PoC (not published). Now it can provide metrics related to salt events and state apply process on the minions. But there is no control on this process implemented yet.

Continue with implementation of the missing features and improve the existing implementation:

• authentication (need to decide how it should be/or not related to salt auth)

• web service providing the control of states deployment

Goal for this Hackweek

• Implement missing key features

• Implement the tool for state deployment control with CLI

Resources

https://github.com/openSUSE/saline

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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

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