ChatGPT has revolutionized test automation. One notable example is the generation of Selenium script, which has become significantly easier.
In this article, we do a deep dive into how you can use ChatGPT to generate Selenium code.
Setting up ChatGPT plugin
Visit the ChatGPT website and download the plugin for your code editor or IDE of your choice. Once installed, configure the plugin by providing your ChatGPT API key or authentication details. You can obtain an API key from the ChatGPT website. This key will enable your code editor to communicate with the ChatGPT model. After configuring the plugin, establish a connection to the ChatGPT model. This step might require an internet connection. Once connected, you can start leveraging the power of ChatGPT within your code editor.
Give ‘Pre-conditions’ and ‘Test conditions’ as input to ChatGPT.
Gather the data that you want to use to populate the "Pre-conditions" and "Test conditions" columns in your Excel file.When using a data-driven framework, you can leverage ChatGPT by providing it with data from an Excel file.
A data-driven framework is an approach to software testing where test cases are created based on data inputs and expected outputs. By utilizing this framework, you can organize your test cases in a structured manner and easily manage large sets of data.
Pre-conditions: These are the initial conditions or setup required before executing a test case. They define the state of the system or application under test before the test scenario is executed. Pre-conditions can include things like:
Data or configurations that need to be present
The system or application is in a specific state
Any prerequisites or dependencies that need to be satisfied
Test conditions: These are the specific conditions or inputs that you want to test in a given scenario. Test conditions are the actions or events that you want to examine during the testing process. They could include:
User interactions or operations
Boundary cases or edge conditions
Different input combinations or scenarios
Call the ChatGPT API and send “pre-conditions” and “test conditions” as inputs to interact with the ChatGPT model.The ChatGPT API will process the input and generate a response based on the provided data.
Create an ArrayList
Create an ArrayList object to store the generated test case steps for selenium script generation. Each test case step should be represented as a string.
In the API request body, you will pass the 'Test case steps array list' as input to ChatGPT. Depending on the API requirements, you may need to serialize the array list. Upon successfully sending the API request, you will receive a response from the ChatGPT API. The response will contain the generated Selenium script based on the provided 'Array list'. Declare and initialize the ArrayList to store the generated Selenium script.
Save the Generated ‘Selenium script’
Once you have the generated Selenium script, add it to the Array List. If you want to generate multiple scripts, repeat the same with different prompts or test case steps. Each generated script can be stored in the ArrayList.
Run the generated ‘Selenium script’
Review the generated script and customize it as needed. Launch the PyCharm Integrated Development Environment (IDE) on your computer. Open PyCharm IDE and create a new Python project by selecting "File" -> "New Project." Choose a name and location for your project and set up the project environment. Before running the generated Selenium script, you need to install the Selenium library. Before running the script, you need to configure the WebDriver to use a specific browser. For example, if you want to use Google Chrome, you need to download the corresponding ChromeDriver executable and provide its path in your script.
Now you are ready to run the Selenium script. Right-click on the Python file containing the script in the project pane and select "Run" or use the keyboard shortcut. PyCharm will execute the script, and the browser controlled by Selenium will perform the actions described in the script.
A notable advantage of the ChatGPT Plugin is its adaptability and extensibility. It can be easily customized to support various programming languages, frameworks, and testing scenarios. By automating the script generation process, users can experience a remarkable increase in productivity, with reported improvements ranging from 40% to 60%, depending on the complexity of the project and the expertise of the user.