AI Testing Prompts [2024 ]
AI Testing Prompts
An AI testing prompt is a specific input or instruction given to an AI tool or LLM model to guide it in generating or executing a test. These prompts are used to instruct the AI on what to test, how to test it, and what kind of results are expected, based on a variety of factors such as the type of software, the functionality being tested, or the testing goals.
In the context of software testing, AI-powered tools (like test automation frameworks, bug detection systems, or test case generation platforms) often require prompts to guide them in performing specific testing tasks. These AI tools use the prompts to understand what scenarios to simulate, what data to use, and how to validate the behavior of the system under test.
Components of AI Prompts
Include the following things in the AI prompt:
Test Type: AI testing prompts often specify the type of test to run (e.g., functional test, performance test, security test, regression test).
Functionality or Component: The prompt can specify which part of the system to test, such as a login form, a search feature, or an API endpoint.
Conditions or Scenarios: Prompts can define specific conditions or scenarios for the test, like testing boundary values, invalid inputs, or simulating a heavy user load.
Expected Outcome: Some prompts may include an expected behavior or result (e.g., “The login should fail if the password is incorrect”), which helps the AI understand what constitutes a pass or fail.
Example
The sample Functionality testing prompt is as below:
“Assume you are a top software testing expert, as a software testing expert, generate test cases for the login feature, including tests for valid credentials, invalid credentials, and empty input fields.”
Unit test case generation
The sample unit testing prompt is as follows:
“Assume you are a top software developer, and generate unit test cases for the current code base.”
Steps to issue the prompt
Follow the below steps to issue the prompts in the IDE. We will use Cursor AI editor in this example.
Launch Cursor IDE. Steps to download and install the Cursor IDE can be found here.
Select the AI Models. Click the top Cursor Settings gear icon >> Models.
Choose the AI models.
Add your application code base.
Enable the Codebase Indexing feature. Embeddings and metadata improve codebase-wide answers.
Cursor settings >> Features >> Codebase Indexing.
Enable the Chat window in the IDE. Type the following key combination in the IDE window.
Ctrl + L or “@.”
Issue the AI prompt in the chatbox.
LLM response:
The model will respond with the unit test cases for the code.
That’s it. Based on the generated test cases, you can further fine-tune the prompt or augment more test cases to ensure code coverage and other test metrics.