Semi-automated XMLRPC based tool for uyuni testing written in Rust language
a project by lkotek
Updated over 4 years ago. 1 hackers ♥️. 1 follower.

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

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.


    • Comments

    Be the first to comment!

    Similar Projects

    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.

    Goals

    • Migrate Core tests including Onboarding of clients
    • Improve test reliabillity: Measure and confirm a significant reduction of flakynes.
    • Implement a robust framework: Establish a well-structured and reusable Playwright test framework using the CucumberJS

    Resources

    Flaky Tests AI Finder for Uyuni and MLM Test Suites by oscar-barrios

    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