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

• Jenkins Prometheus Exporter: https://github.com/uyuni-project/jenkins-exporter/
• Data Source: Our internal Prometheus server.
• Key Metric: jenkins_build_test_case_failure_age{jobname, buildid, suite, case, status, failedsince}.
• Existing Query for Reference: count by (suite) (max_over_time(jenkins_build_test_case_failure_age{status=~"FAILED|REGRESSION", jobname="$jobname"}[$__range])).
• AI Model: The Google Gemini API.
• Example about how to interact with Gemini API: https://github.com/srbarrios/FailTale/
• Visualization: Our internal Grafana Dashboard.
• Internal IaC: https://gitlab.suse.de/galaxy/infrastructure/-/tree/master/srv/salt/monitoring

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

• Existing Uyuni Test Framework (Cucumber Ruby + Capybara + Selenium)
• My Template for CucumberJS + Playwright in TypeScript
• Started Hackweek Project

Advent of Code: The Diaries by amanzini

Description

It was the Night Before Compile Time ...

Hackweek 25 (December 1-5) perfectly coincides with the first five days of Advent of Code 2025. This project will leverage this overlap to participate in the event in real-time.

To add a layer of challenge and exploration (in the true spirit of Hackweek), the puzzles will be solved using a non-mainstream, modern language like D, Crystal, Gleam or Zig.

The primary project intent is not just simply to solve the puzzles, but to exercise result sharing and documentation. I'd create a public-facing repository documenting the process. This involves treating each day's puzzle as a mini-project: solving it, then documenting the solution with detailed write-ups, analysis of the language's performance and ergonomics, and visualizations.

                               |
                             \ ' /
                           -- (*) --
                              >*<
                             >0<@<
                            >>>@<<*
                           >@>*<0<<<
                          >*>>@<<<@<<
                         >@>>0<<<*<<@<
                        >*>>0<<@<<<@<<<
                       >@>>*<<@<>*<<0<*<
         \*/          >0>>*<<@<>0><<*<@<<
     ___\\U//___     >*>>@><0<<*>>@><*<0<<
     |\\ | | \\|    >@>>0<*<0>>@<<0<<<*<@<<
     | \\| | _(UU)_ >((*))_>0><*<0><@<<<0<*<
     |\ \| || / //||.*.*.*.|>>@<<*<<@>><0<<<
     |\\_|_|&&_// ||*.*.*.*|_\\db//_
     """"|'.'.'.|~~|.*.*.*|     ____|_
         |'.'.'.|   ^^^^^^|____|>>>>>>|
         ~~~~~~~~         '""""`------'
------------------------------------------------
This ASCII pic can be found at
https://asciiart.website/art/1831

Goals

Code, Docs, and Memes: An AoC Story

• Have fun!

• Involve more people, play together

• Solve Days 1-5: Successfully solve both parts of the Advent of Code 2025 puzzles for Days 1-5 using the chosen non-mainstream language.

• Daily Documentation & Language Review: Publish a detailed write-up for each day. This documentation will include the solution analysis, the chosen algorithm, and specific commentary on the language's ergonomics, performance, and standard library for the given task.