Automated Test Report reviewer
an idea by oscar-barrios
Updated about 1 year ago. 2 hacker ♥️. 1 follower. Has no hacker: grab it!

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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uyuni ai reports testing

This project is part of:

Hack Week 24

Activity

  • about 1 year ago: juliogonzalezgil liked this project.
  • about 1 year ago: livdywan liked this project.
  • about 1 year ago: oscar-barrios added keyword "uyuni" to this project.
  • about 1 year ago: oscar-barrios added keyword "ai" to this project.
  • about 1 year ago: oscar-barrios added keyword "reports" to this project.
  • about 1 year ago: oscar-barrios added keyword "testing" to this project.
  • about 1 year ago: oscar-barrios originated this project.


    • Comments

    • oscar-barrios
      about 1 year ago by oscar-barrios | Reply

    • oscar-barrios
      2 months ago by oscar-barrios | Reply

      I end up continuing this project on my free time, and I made some progress here: https://github.com/srbarrios/FailTale

    Similar Projects

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    Description

    Our current Grafana dashboards provide a great overview of test suite health, including a panel for "Top failed tests." However, identifying which of these failures are due to legitimate bugs versus intermittent "flaky tests" is a manual, time-consuming process. These flaky tests erode trust in our test suites and slow down development.

    This project aims to build a simple but powerful Python script that automates flaky test detection. The script will directly query our Prometheus instance for the historical data of each failed test, using the jenkins_build_test_case_failure_age metric. It will then format this data and send it to the Gemini API with a carefully crafted prompt, asking it to identify which tests show a flaky pattern.

    The final output will be a clean JSON list of the most probable flaky tests, which can then be used to populate a new "Top Flaky Tests" panel in our existing Grafana test suite dashboard.

    Goals

    By the end of Hack Week, we aim to have a single, working Python script that:

    1. Connects to Prometheus and executes a query to fetch detailed test failure history.
    2. Processes the raw data into a format suitable for the Gemini API.
    3. Successfully calls the Gemini API with the data and a clear prompt.
    4. Parses the AI's response to extract a simple list of flaky tests.
    5. Saves the list to a JSON file that can be displayed in Grafana.
    6. New panel in our Dashboard listing the Flaky tests

    Resources

    Outcome

    Move Uyuni Test Framework from Selenium to Playwright + AI by oscar-barrios

    Description

    This project aims to migrate the existing Uyuni Test Framework from Selenium to Playwright. The move will improve the stability, speed, and maintainability of our end-to-end tests by leveraging Playwright's modern features. We'll be rewriting the current Selenium code in Ruby to Playwright code in TypeScript, which includes updating the test framework runner, step definitions, and configurations. This is also necessary because we're moving from Cucumber Ruby to CucumberJS.

    If you're still curious about the AI in the title, it was just a way to grab your attention. Thanks for your understanding.

    Nah, let's be honest add-emoji AI helped a lot to vibe code a good part of the Ruby methods of the Test framework, moving them to Typescript, along with the migration from Capybara to Playwright. I've been using "Cline" as plugin for WebStorm IDE, using Gemini API behind it.

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    Resources

    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):

    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 :-)

    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

    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

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    Description

    Prepare a Poc on how to use MLM to manage edge clusters. Those cluster are normally equal across each location, and we have a large number of them.

    The goal is to produce a set of sets/best practices/scripts to help users manage this kind of setup.

    Goals

    step 1: Manual set-up

    Goal: Have a running application in k3s and be able to update it using System Update Controler (SUC)

    • Deploy Micro 6.2 machine

    • Deploy k3s - single node

      • https://docs.k3s.io/quick-start

    • Build/find a simple web application (static page)

      • Build/find a helmchart to deploy the application

    • Deploy the application on the k3s cluster

    • Install App updates through helm update

    • Install OS updates using MLM

    step 2: Automate day 1

    Goal: Trigger the application deployment and update from MLM

    • Salt states For application (with static data)
      • Deploy the application helmchart, if not present
      • install app updates through helmchart parameters
    • Link it to GIT
      • Define how to link the state to the machines (based in some pillar data? Using configuration channels by importing the state? Naming convention?)
      • Use git update to trigger helmchart app update
    • Recurrent state applying configuration channel?

    step 3: Multi-node cluster

    Goal: Use SUC to update a multi-node cluster.

    • Create a multi-node cluster
    • Deploy application
      • call the helm update/install only on control plane?
    • Install App updates through helm update
    • Prepare a SUC for OS update (k3s also? How?)
      • https://github.com/rancher/system-upgrade-controller
      • https://documentation.suse.com/cloudnative/k3s/latest/en/upgrades/automated.html
      • Update/deploy the SUC?
      • Update/deploy the SUC CRD with the update procedure

    Set Up an Ephemeral Uyuni Instance by mbussolotto

    Description

    To test, check, and verify the latest changes in the master branch, we want to easily set up an ephemeral environment.

    Goals

    • Create an ephemeral environment manually

    • Create an ephemeral environment automatically

      Resources

    • https://github.com/uyuni-project/uyuni

    • https://www.uyuni-project.org/uyuni-docs/en/uyuni/index.html

    SUSE Edge Image Builder MCP by eminguez

    Description

    Based on my other hackweek project, SUSE Edge Image Builder's Json Schema I would like to build also a MCP to be able to generate EIB config files the AI way.

    Realistically I don't think I'll be able to have something consumable at the end of this hackweek but at least I would like to start exploring MCPs, the difference between an API and MCP, etc.

    Goals

    • Familiarize myself with MCPs
    • Unrealistic: Have an MCP that can generate an EIB config file

    Resources

    Result

    https://github.com/e-minguez/eib-mcp

    I've extensively used antigravity and its agent mode to code this. This heavily uses https://hackweek.opensuse.org/25/projects/suse-edge-image-builder-json-schema for the MCP to be built.

    I've ended up learning a lot of things about "prompting", json schemas in general, some golang, MCPs and AI in general :)

    Example:

    Generate an Edge Image Builder configuration for an ISO image based on slmicro-6.2.iso, targeting x86_64 architecture. The output name should be 'my-edge-image' and it should install to /dev/sda. It should deploy a 3 nodes kubernetes cluster with nodes names "node1", "node2" and "node3" as: * hostname: node1, IP: 1.1.1.1, role: initializer * hostname: node2, IP: 1.1.1.2, role: agent * hostname: node3, IP: 1.1.1.3, role: agent The kubernetes version should be k3s 1.33.4-k3s1 and it should deploy a cert-manager helm chart (the latest one available according to https://cert-manager.io/docs/installation/helm/). It should create a user called "suse" with password "suse" and set ntp to "foo.ntp.org". The VIP address for the API should be 1.2.3.4

    Generates:

    ``` apiVersion: "1.0" image: arch: x86_64 baseImage: slmicro-6.2.iso imageType: iso outputImageName: my-edge-image kubernetes: helm: charts: - name: cert-manager repositoryName: jetstack

    SUSE Observability MCP server by drutigliano

    Description

    The idea is to implement the SUSE Observability Model Context Protocol (MCP) Server as a specialized, middle-tier API designed to translate the complex, high-cardinality observability data from StackState (topology, metrics, and events) into highly structured, contextually rich, and LLM-ready snippets.

    This MCP Server abstract the StackState APIs. Its primary function is to serve as a Tool/Function Calling target for AI agents. When an AI receives an alert or a user query (e.g., "What caused the outage?"), the AI calls an MCP Server endpoint. The server then fetches the relevant operational facts, summarizes them, normalizes technical identifiers (like URNs and raw metric names) into natural language concepts, and returns a concise JSON or YAML payload. This payload is then injected directly into the LLM's prompt, ensuring the final diagnosis or action is grounded in real-time, accurate SUSE Observability data, effectively minimizing hallucinations.

    Goals

    • Grounding AI Responses: Ensure that all AI diagnoses, root cause analyses, and action recommendations are strictly based on verifiable, real-time data retrieved from the SUSE Observability StackState platform.
    • Simplifying Data Access: Abstract the complexity of StackState's native APIs (e.g., Time Travel, 4T Data Model) into simple, semantic functions that can be easily invoked by LLM tool-calling mechanisms.
    • Data Normalization: Convert complex, technical identifiers (like component URNs, raw metric names, and proprietary health states) into standardized, natural language terms that an LLM can easily reason over.
    • Enabling Automated Remediation: Define clear, action-oriented MCP endpoints (e.g., execute_runbook) that allow the AI agent to initiate automated operational workflows (e.g., restarts, scaling) after a diagnosis, closing the loop on observability.

     Hackweek STEP

    • Create a functional MCP endpoint exposing one (or more) tool(s) to answer queries like "What is the health of service X?") by fetching, normalizing, and returning live StackState data in an LLM-ready format.

     Scope

    • Implement read-only MCP server that can:
      • Connect to a live SUSE Observability instance and authenticate (with API token)
      • Use tools to fetch data for a specific component URN (e.g., current health state, metrics, possibly topology neighbors, ...).
      • Normalize response fields (e.g., URN to "Service Name," health state DEVIATING to "Unhealthy", raw metrics).
      • Return the data as a structured JSON payload compliant with the MCP specification.

    Deliverables

    • MCP Server v0.1 A running Golang MCP server with at least one tool.
    • A README.md and a test script (e.g., curl commands or a simple notebook) showing how an AI agent would call the endpoint and the resulting JSON payload.

    Outcome A functional and testable API endpoint that proves the core concept: translating complex StackState data into a simple, LLM-ready format. This provides the foundation for developing AI-driven diagnostics and automated remediation.

    Resources

    • https://www.honeycomb.io/blog/its-the-end-of-observability-as-we-know-it-and-i-feel-fine
    • https://www.datadoghq.com/blog/datadog-remote-mcp-server
    • https://modelcontextprotocol.io/specification/2025-06-18/index
    • https://modelcontextprotocol.io/docs/develop/build-server

     Basic implementation

    • https://github.com/drutigliano19/suse-observability-mcp-server

    Results

    Successfully developed and delivered a fully functional SUSE Observability MCP Server that bridges language models with SUSE Observability's operational data. This project demonstrates how AI agents can perform intelligent troubleshooting and root cause analysis using structured access to real-time infrastructure data.

    Example execution

    Self-Scaling LLM Infrastructure Powered by Rancher by ademicev0

    Self-Scaling LLM Infrastructure Powered by Rancher

    logo

    Description

    The Problem

    Running LLMs can get expensive and complex pretty quickly.

    Today there are typically two choices:

    1. Use cloud APIs like OpenAI or Anthropic. Easy to start with, but costs add up at scale.
    2. Self-host everything - set up Kubernetes, figure out GPU scheduling, handle scaling, manage model serving... it's a lot of work.

    What if there was a middle ground?

    What if infrastructure scaled itself instead of making you scale it?

    Can we use existing Rancher capabilities like CAPI, autoscaling, and GitOps to make this simpler instead of building everything from scratch?

    Project Repository: github.com/alexander-demicev/llmserverless

    What This Project Does

    A key feature is hybrid deployment: requests can be routed based on complexity or privacy needs. Simple or low-sensitivity queries can use public APIs (like OpenAI), while complex or private requests are handled in-house on local infrastructure. This flexibility allows balancing cost, privacy, and performance - using cloud for routine tasks and on-premises resources for sensitive or demanding workloads.

    A complete, self-scaling LLM infrastructure that:

    • Scales to zero when idle (no idle costs)
    • Scales up automatically when requests come in
    • Adds more nodes when needed, removes them when demand drops
    • Runs on any infrastructure - laptop, bare metal, or cloud

    Think of it as "serverless for LLMs" - focus on building, the infrastructure handles itself.

    How It Works

    A combination of open source tools working together:

    Flow:

    • Users interact with OpenWebUI (chat interface)
    • Requests go to LiteLLM Gateway
    • LiteLLM routes requests to:
      • Ollama (Knative) for local model inference (auto-scales pods)
      • Or cloud APIs for fallback

    Is SUSE Trending? Popularity and Developer Sentiment Insight Using Native AI Capabilities by terezacerna

    Description

    This project aims to explore the popularity and developer sentiment around SUSE and its technologies compared to Red Hat and their technologies. Using publicly available data sources, I will analyze search trends, developer preferences, repository activity, and media presence. The final outcome will be an interactive Power BI dashboard that provides insights into how SUSE is perceived and discussed across the web and among developers.

    Goals

    1. Assess the popularity of SUSE products and brand compared to Red Hat using Google Trends.
    2. Analyze developer satisfaction and usage trends from the Stack Overflow Developer Survey.
    3. Use the GitHub API to compare SUSE and Red Hat repositories in terms of stars, forks, contributors, and issue activity.
    4. Perform sentiment analysis on GitHub issue comments to measure community tone and engagement using built-in Copilot capabilities.
    5. Perform sentiment analysis on Reddit comments related to SUSE technologies using built-in Copilot capabilities.
    6. Use Gnews.io to track and compare the volume of news articles mentioning SUSE and Red Hat technologies.
    7. Test the integration of Copilot (AI) within Power BI for enhanced data analysis and visualization.
    8. Deliver a comprehensive Power BI report summarizing findings and insights.
    9. Test the full potential of Power BI, including its AI features and native language Q&A.

    Resources

    1. Google Trends: Web scraping for search popularity data
    2. Stack Overflow Developer Survey: For technology popularity and satisfaction comparison
    3. GitHub API: For repository data (stars, forks, contributors, issues, comments).
    4. Gnews.io API: For article volume and mentions analysis.
    5. Reddit: SUSE related topics with comments.

    Explore LLM evaluation metrics by thbertoldi

    Description

    Learn the best practices for evaluating LLM performance with an open-source framework such as DeepEval.

    Goals

    Curate the knowledge learned during practice and present it to colleagues.

    -> Maybe publish a blog post on SUSE's blog?

    Resources

    https://deepeval.com

    https://docs.pactflow.io/docs/bi-directional-contract-testing

    Multimachine on-prem test with opentofu, ansible and Robot Framework by apappas

    Description

    A long time ago I explored using the Robot Framework for testing. A big deficiency over our openQA setup is that bringing up and configuring the connection to a test machine is out of scope.

    Nowadays we have a way¹ to deploy SUTs outside openqa, but we only use if for cloud tests in conjuction with openqa. Using knowledge gained from that project I am going to try to create a test scenario that replicates an openqa test but this time including the deployment and setup of the SUT.

    Goals

    Create a simple multimachine test scenario with the support server and SUT all created by the robot framework.

    Resources

    1. https://github.com/SUSE/qe-sap-deployment
    2. terraform-libvirt-provider

    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):

    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 :-)

    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

    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