Update developer guide

doc/developer_guide/testing.md

@@ -1,20 +1,51 @@
-### Tests
+# Tests
 
-XAL comes with a number of tests in directory `test`.
-Besides, unit and integrations tests in the respective directories
-there are tests in directory `codebuild`, see [Executing AWS CodeBuild](ci.md#executing-aws-codebuild).
+XAIL comes with a number of tests in directory `test`.  Besides, unit and integrations tests in the respective directories there are tests in directory `codebuild`, see [Executing AWS CodeBuild](ci.md#executing-aws-codebuild).
 
-# Speeding up Docker-based Tests
+## Speeding up Docker-based Tests
 
-Creating a docker image is quite time-consuming, currently around 7 minutes. In order to use an existing
-docker image in the tests in `integration/test_create_dss_docker_image.py`
-simply add CLI option `--dss-docker-image` when calling `pytest`:
+Creating a docker image is quite time-consuming, currently around 7 minutes.
+
+For getting test results faster you can use an existing Docker image.  You can create such an image using the [CLI command](commands.md#release-commands) `create-docker-image` or run your tests once with an additional CLI option `--keep-dss-docker-image` to keep the image rather then removing it after the test session.
+
+Sample lusage of command `create-docker-image`:
+```shell
+poetry run exasol/ds/sandbox/main.py \
+       create-docker-image \
+       --version 9.9.9 \
+       --log-level info
+```
+
+In order to use an existing docker image in the tests in `integration/test_create_dss_docker_image.py` simply add CLI option `--dss-docker-image` when calling `pytest`:
 
 ```shell
 poetry run pytest --dss-docker-image exasol/ai-lab:2.1.0
 ```
 
-#### Executing tests involving AWS resources
+## Tests for Juypter Notebooks
+
+The AI-Lab also contains end-to-end tests for Jupyter notebooks. Executing these tests can take several hours, currently ~ 3h.
+
+The notebook tests are based on a common parameterized [test-runner](../../test/notebook_test_runner/test_notebooks_in_dss_docker_image.py).  The test-runner contains a single parameterized test case on the outer level.  Each time the test is executed, the test is parameterized with a python file from directory [test/notebooks](../../test/notebooks/) containing the particular testcases for one of the Jupyter notebooks.
+
+The outer test case then uses a session-scoped fixture for creating an ordinary AI-Lab Docker image. Another session-scoped fixture adds some packages for executing the notebook tests, resulting in 2nd Docker image.  Finally the test-runner launches a Docker container from the 2nd image and runs the inner test cases for the current notebook inside the Docker container.
+
+In total the following Docker entities are involved
+* Docker image 1 of the AI-Lab
+* Docker image 2 for running the inner notebook tests
+* Docker container running the Docker image 2
+
+### Speeding up Notebook Tests
+
+You can speed up the notebook tests using the [same strategy](#speeding-up-docker-based-tests) as for tests involving the basic Docker image for the AI-Lab.
+
+The CLI option to keep the image is `--keep-docker-image-notebook-test`, the option for using an existing Docker image for executing the notebook tests is `--docker-image-notebook-test`.
+
+```shell
+poetry run pytest --docker-image-notebook-test <name:version>
+```
+
+## Executing Tests Involving AWS Resources
 
 In AWS web interface, IAM create an access key for CLI usage and save or download the *access key id* and the *secret access key*.
 
@@ -42,10 +73,10 @@ export AWS_PROFILE=dss_aws_tests_mfa
 poetry run pytest test/test_deploy_codebuild.py
 ```
 
-#### Executing tests involving Ansible
+## Executing Tests Involving Ansible
 
 For making pytest display Ansible log messages, please use
 
 ```shell
 poetry run pytest -s -o log_cli=true -o log_cli_level=INFO
-```
+```