Artificial Intelligence is quickly spreading across all areas of professional life.
Whether it is about using its features like predictive analytics or machine learning to enhance the decision-making power of the systems, or generating content to support business operations, AI is making its mark in all areas of business.
While features like predictive algorithms and machine learning can be incorporated into the code, Generative AI is primarily dependent on its interaction with the user.Ā
ALSO READ: ReAct Prompting Technique
When an AI system is using Natural Language Processing (NLP) algorithms at the backend to interact meaningfully with the user, it becomes imperative for the users, too, to provide instructions most effectively to derive intended results.
This is where prompt engineering and multi-step prompting come into play.
Through this, the user can provide clear and sequential instructions to the AI systems, which can be used to generate various kinds of content, including test cases.
This article specifically explores the role of multi-step prompting in generating the most effective test cases and the strategies you can use to achieve this objective.
Prompting in General
When an AI system starts, it is in a totally blank state, waiting for your instructions.
You may have certain context already in your mind when you instruct the AI, but from the AIās point of view, it literally takes your words and generates meaning in a totally random context.
It may or may not be aligned with you. Hence, your prompt should be very specific and clear for it to not only understand the words, but also the contextual meaning it should account for to perform the needed task.
A broken or incomplete prompt may cause it to behave absurdly, producing irrelevant or inaccurate output.Ā
When creating test cases, if you give a generic prompt, like āproduce a test case to validate the check-out processā, it will create very shallow test scenarios that might miss out on edge cases, important validations, and failure scenarios.
You have to keep each and every situation in mind that you want to test and give your prompts specifically to generate test cases for them.
This is why prompt engineering is emerging as a core skill for modern QA engineers who are performing their tests using an AI testing tool.Ā
Advanced prompting architectures, like multi-step prompts, encourage structured thinking, where complex tasks are broken down into sequential logical steps.
Applying this strategy to test case generation allows for enhanced quality control and better test coverage of your application.
A typical multi-step prompting for test case generation may go through the following steps:
This layered approach is not much different from the human reasoning method.
The step-by-step instructions allow AI to use its reasoning sequentially and produce better outcomes.
That results in better test coverage along with deeper details in each of them.
The best strategy for effective multi-step prompting is to adopt a layered approach as briefly described in the previous section.
Letās expand them in detail now.
Start with a Broader Context
For an AI system to get familiar with your application and set the context, start by giving a brief overview of your application.
In AI-powered test automation tools, like testRigor, prompt engineering in software testing starts with providing a detailed description of the application.
Provide information about what the applicationās main goal is, what its features are, and its core workflows.
This ensures that AI registers the scope of its reasoning for further development.
Generate Positive Test Scenarios
Once the context is set, you should prompt for generating test cases that validate the basic functionality of the application.
This includes testing all the features, labels, workflows, and functionality of the application in an ideal scenario.
When an expected input is provided to the application, the primary functionality must produce the desired objectives.
Produce Negative Test Scenarios
Negative test cases are used for testing an applicationās response when the wrong input is given to it.
E.g., a mandatory field may be left blank, or an invalid PIN code is given.
While these are input-related edge cases, you may also generate test cases to check the performance of the application in system constraints.
Negative test cases ensure that your application doesnāt break in unforeseen conditions.
Provide Validation Rules
The test cases would be incomplete until the AI knows the validation rules.
That means it should check the functionality of the application against pre-defined validation rules,
e.g., if the error message that you provide in the prompt is being displayed correctly or not during an edge test case, or if the data update is done correctly or not.
Perform Multiple Iterations
Once you have covered all the above steps in prompts, the AI has gotten familiar with the scope and logic for generating the test cases.
Further refinement in the test cases becomes even more feasible at this stage.
You need to perform multiple iterations in the test cases to make them fit precisely to your testing needs.
Prompt engineering is a rapidly growing skill because of the progressive adoption of AI in modern testing applications.
While AI systems are evolving every day to better comprehend human interaction, human users are also needed to think in the way AI creates meaning.
Itās this two-way meaning exchange process that produces the best outcome.Ā
Multi-step prompting is one of the greatest developments towards enhancing this human-AI interaction.
It teaches us to think and provide instructions to the AI in the best possible manner it would understand to provide the desired outcomes.
And when this approach is applied to the testing strategies, it can take the quality of your product to a whole new level.
Test cases generation through multi-step prompting not only saves time but also enhances the test coverage considerably, thereby turning your application into a robust system.
