Import feature file
Hiptest enables you to upload feature files written using Gherkin syntax (Cucumber, SpecFlow…)
In your project settings, activate the BDD mode option.
This will enable the step editor to interpret the Gherkin syntax and only action words (reusable steps) will be created.
For a good project organization, your features are managed with folders.
After activating the BDD mode, in the folder page, new buttons appear:
You can upload your feature files by clicking on one of the buttons or using the drag-and-drop function.
Note: Only “.feature” extension is accepted. Otherwise an error will be displayed.
The button [Create From Feature File] and the drop zone will first look for existing subfolders with the same name as the features you are importing.
If none can be found then new subfolders are created under the current folder. They represent the imported features with their descriptions, tags, scenarios, scenario outlines, examples…
Otherwise, as the [Update From Feature File] button, existing folders will be updated with the features you are importing. For this purpose a window will show you which updates are about to be done:
Click [Apply Changes] once you review them to update your folder and scenarios.
The Gherkin steps will be converted into action word calls. If the action word does not exist, it will be created.
If there is an existing folder with the same name in the same hierarchy level, the new folder name will be suffixed with a number. For example, an existing folder is named “Support internationalisation”, the new one will be named “Support internationalisation (1)”.
If one or more scenarios in your folder are not present in the .feature file they will remain in your folder unless you checked the option to delete them.
In that case they will be permanently deleted from your folder.
When updating a folder from a feature file that contains scenario outlines with examples, datasets of impacted scenarios will be updated accordingly.
As Gherkin examples are not identified, datasets are retrieved based on their values. If a value has changed the corresponding dataset will not be found. A new one will be created and the old one will be deleted.