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TESTING.md

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Testing documentation

To start the automatic test, you can use the following command:

python -m unittest discover -s tests

You also have the option to run automatic test in run_test.py or manually test the functionality with the frontend. See frontend testing documentation

To perform the following test, you will need the frontend repository for the Nachet Interactive's application. The frontend can be found at: Nachet Frontend GitHub Repository.

You will also need the list of the available pipelines. The list can be found here.

Test Case: Populate model selection with pipelines information

Objective: Verify that the model selection component gets populated with the pipeline information.

Preconditions:

  • Nachet backend is set up and running. Use the command hypercorn -b :8080 app:app to start the quart server.
  • The environment variables are all set.
  • ❗ The frontend is not running yet

Test Steps:

  1. Start the frontend application
  2. Click on the model selection button
  3. Validate that the current pipeline is selectable.

Expected Results:

  • If a problem occurs while retrieving the data, an error should prevent the server from starting.
  • If a problem occurs while retrieving the data, but no error was raised, the model selection component should be empty.
  • If everything went correctly while retrieving the data, the model selection component should display the pipeline metadata.

Actual Results:

  • Describe the actual outcome of the test

Pass/Fail Criteria:

  • Pass if the metadata from the available pipeline is displayed.
  • Fail if the metadata from the available pipeline is not displayed and no error was raised.

Test Case: Inference Request

Objective: Verify that the inference request endpoint /inf behaves as expected.

Preconditions:

  • Nachet backend is set up and running. Use the command hypercorn -b :8080 app:app to start the quart server.
  • The environment variables are all set.
  • The frontend is running.
  • Start the frontend application
  • Click on the model selection button
  • Validate that the current pipeline is selectable

Test Steps:

  1. Upload a seed image
  2. Select the first model (pipeline)
  3. Click on the classify button
  4. Wait until the results populated on the canvas
  5. Repeat the process for every model (pipeline)

|:boom: Warning| |:--:| |Displaying results from two different models will overlap and become unreadable.|

Expected Results:

  • The data populates both the canvas and the results components with the prediction data from the model (pipeline).
  • An alert with an error from port 3000 or 8080 is displayed.

Actual Results:

  • Describe the actual outcome of the test

Pass/Fail Criteria:

  • Pass if the data populates both the canvas and the results components with the prediction of the model (pipeline).
  • Fail if an alert is displayed with an error message.
  • Fail if the data does not populates the canvas and the results component
  • Fail if the inference is stuck in an infinite loop