Health Dashboard will allow a user to chart exported Apple Health data to explore correlations.
Frontend will be based on ReactJS Backend will be Go based with a Prometheus Time series store.
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Hack Week 17
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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.
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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:
- Connects to Prometheus and executes a query to fetch detailed test failure history.
- Processes the raw data into a format suitable for the Gemini API.
- Successfully calls the Gemini API with the data and a clear prompt.
- Parses the AI's response to extract a simple list of flaky tests.
- Saves the list to a JSON file that can be displayed in Grafana.
- 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
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- https://github.com/updatecli/updatecli
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Description
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Description
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