The log files of openQA jobs are key to reviewing the result of a test. What if they could be processed and analyzed with the help of AI? More specifically using TensorFlow to train a model on passing and failing jobs.
The quickest way to run testimony is via the container:
podman run -it --rm -v $(pwd):/w -w /w ghcr.io/kalikiana/testimony/min:latestFor a typical development setup the necessary dependencies can be installed via poetry:
poetry install
poetry run pytest -v