Description
The openQA test framework actually we need to keep stored many screenshots and variants of GUI and CLI-terminal images, scanning the many pictures in the repository to match our target based on a specific process, selecting little parts of each stored image with the target SUT image.
A smarter framework using image recognition, besed on AI or image elaborations tools, nowadays widely available, could improve the matching process and hopefully reduce time (and errors) needed for needles/reference-images verification process, comparing the SUT graphic object with the proper tools.
Goals
Main scope of the idea is to match a grafical 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 preparation and use, than the actual openQA needles framework, that is:
- having a given SUT's GUI or CLI-terminal screenshot, with a distribution of data or commands related to a running test status,
- we want to identify a desired target, an 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 needles framwork.
- Expected an improvement of the matching-time, the reliabily of the expected result and simplification of archive maintenance in adding/removing objects.
Resources
- Identification of useful resources is the first step of this project; possible are:
- SUSE AI and other ML tools (i.e. Tensorflow)
- Tools able to manage images
- RPA test tools (like i.e. Robot framework)
- other.
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Hack Week 25
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Join the Gitter channel! https://gitter.im/uyuni-project/hackweek
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