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Price upon request
|Monthly Testing Minutes||unlimited||unlimited||unlimited||unlimited|
|Maximum Tests (*When reaching the limit, it's possible to delete tests in order to create new ones)||5||30||unlimited||unlimited|
|Price for Additional Seat||€50 / month||€50 / month||€50 / month||€50 / month|
|Test Batch Composer||✔||✔||✔||✔|
|Pixel Perfect Assertions||✔||✔||✔||✔|
|Test Reporting||PDF / JIRA||PDF / JIRA|
|Test Batch Reporting (ETA Q2/2021)||✔||✔|
|Test sharing between workstations||✔|
|Workspaces (work environments)||✔|
|Contiuous Integration Support||✔|
|Priority Support via Email & Slack||✔|
A real alternative to Espresso
Tired of trying to find the right view ID, mocking classes, creating idling resources or finding the right delays for your test automation? This is how repeato does things differently:
Common UI Testing Frameworks need to have a sophisticated understanding of how the UI is structured. In order to create asserts and click views, the testing framework needs a unique reference to the respective view in the view hierarchy (layout). Usually a manually set ID or the "layout path" is used as a unique reference and the developers needs to reference that ID when they write test automation code.
This approach comes with a lot of complexity which needs to be handled by the developer:
- Looking up view IDs
- Making sure that those are unique (IDs can reoccur in different places in the hierarchy)
- When layouts are refactored IDs often change, this breaks the tests, which need to be fixed
- Creating idling resources for asynchronous code
- Making sure view lookups and clicks are not executed before view is fully visible (problematic with view animations and fragment transitions)
- Installing additional framework components for handling automation outside of the app context (notifications, switching to other app)
Repeato does things differently. It operates fully on pixel data and does not care about the underlying structure of the UI. It basically sees what a tester would see by utilising computer vision (open CV) and machine learning (tensor flow).
Asserts are created by defining pixel regions, which can later be found again by the computer vision algorithm. Even when your design or device aspect rations change slightly.