A universal automation framework to handle mobile testing, web testing, api testing in a single powerful python package with capabilities like device farming and so on.
- Web automation
- API automation
- Mobile automation
- Device farming
- ChatGpt integration
- Allure docker service integration
- knowledge of appium
- knowledge of selenium
- knowledge of python
- knowledge of api testing
There are two ways in which the framework can be utilised:
- Build the package and install it to current working directory
- Or, utilise the framework as is by creating a testing layer
- Prep the system:
- Install docker
- Install appium
- Install appium inspector
- Install android studio
- cli tools and sdkmanager need to be properly installed and configured, as in the later part these are required in automatic creation of emulators for testing using device farming
- avdmanager, sdkmanager command availability in the terminal/cmd/powershell
- Install xcode - Optional - applicable only to mac device users
- Download python 3.11, minimum requirement >= 3.11
- Install pipenv as, it is used as a defacto for the package manager
pip install pipenv Note: based on the python installation or configuration, the keyword for pip can be either pip/pip3
- Prep project:
- There is an inbuilt device farming capability and for this we need to execute below command
sudo docker compose -f docker-compose-device-farm.yml up --build -or- sudo docker compose -f docker-compose-device-farm.yml up -d --build
- Once the docker containers are up, next thing is to prep the mongodb and add few data so that it starts working
- Create a database called appium_device_stats
- In the database which has been created, create two collections
- device_stats
- Holds data pertaining to device availability
- device_sessions
- Holds data pertaining to device sessions
- device_stats
- Now the MongoDb and RabbitMQ are configured, let's head further steps which lead us to completion
- Install project dependencies
pipenv run make install
- Invoke celery, this is required for device farming to work
pipenv run celery -A uaf.device_farming.device_tasks worker -B -E -O fair --loglevel=INFO
- Build the package - Optional
pipenv run make build
- Install the package to current pipenv package manager - Optional
pipenv run make install-package
- Note: Once executed, all the tests should pass, otherwise please correct the mistakes and proceed further
- To visualise the report through allure docker service, docker containers have to be invoked. Use the below commands for the same
sudo docker compose -f docker-compose-report.yml up -d --build
- Currently the project hosts sensitive data, which is encrypted using in house encryption using cryptography lib and since the file is encrypted and will remain encrypted indefinetly. Below is the template that needs to be followed for the same, at least initially to make the scripts and the project work. Later it can be modified according to the taste of individuals/ teams
info: name: Common ports: appipum_service_min_port_band: <min_port_number> appium_service_max_port_band: <max_port_number> appium: appium_base_url_local: http://localhost:${port}/wd/hub appium_base_url_remote: http://localhost:${port}/wd/hub celery: broker_url: amqp://<username>:<password>@localhost:5672 result_backend: rpc://<username>>:<password>@localhost:5672 mongodb: connection_string: mongodb://<username>>:<password>@localhost:27018/appium_device_stats?authSource=admin&authMechanism=SCRAM-SHA-256 device_stat_collection: device_stats device_session_collection: device_sessions chatgpt: api_key: <chat_gpt_api_key> engine: <chat_gpt_model> max_tokens: <max_token> temperature: <temperature> waits: max_time_out: <max_time_out_time_in_seconds_for_webdriver_wait> - To encrypt/decrypt sensitive information, use the generated AES-256 key
- If there is no AES-256 key present or if it is the first time that a script is being run then follow the below steps
- Open a python console which is pointing to project root and type the below
python cli.py --mode generate_key - Copy the generated key and store it in the project directory inside a .env file for reference, create one if not present
- Open a python console which is pointing to project root and type the below
- Now that we have a key handy, we can proceed with the sensitive data file encryption or decryption depending on the scenario
- To encrypt the data file
python cli.py --mode encrypt --key <generated_secret_key> --data_file <relative_file_path> - To decrypt the data file
python cli.py --mode decrypt --key <generated_secret_key> --data_file <relative_file_path>
- To encrypt the data file
- If there is no AES-256 key present or if it is the first time that a script is being run then follow the below steps
-
Now everything is setup and running fine, one final thing to test if things are really working. To run tests, run the following command
pytest -or- pytest -v <relative_path_testclass_py_file> -or- pytest -v <relative_path_testclass_py_file>::<testcase_method_name> -or- pytest -v -m <tag_name> -
To run the test parallelly
pytest -n <number_of_parallel_threads> -or- pytest -v <relative_path_testclass_py_file> -n <number_of_parallel_threads> -or- pytest -v -m <tag_name> -n <number_of_parallel_threads> -
To run the test and visualise report using allure
- The allure reports will be available in the allure-reports which can be found in the project root
pytest -n <number_of_parallel_threads> --alluredir=allure-results -or- pytest -v <relative_path_testclass_py_file> -n <number_of_parallel_threads> --alluredir=allure-results -or- pytest -v -m <tag_name> -n <number_of_parallel_threads> --alluredir=allure-results
- The allure reports will be available in the allure-reports which can be found in the project root
-
For more information on pytest, feel free to read the docs