Blog

December 17, 2024

The AI Testing Evolution: The Future of QA Professionals & Prompt Engineering With AI

Clinton Sprauve
Clinton Sprauve

Artificial Intelligence (AI)

One of the most pervasive myths in software testing is that AI will eventually replace manual testers, rendering human involvement unnecessary. While AI will undoubtedly transform testing, it will not eliminate the need for human testers.  

Instead, one of the most promising areas of evolution is the rise of advanced prompt engineering for test creation, where testers will act as orchestrators, guiding generative AI to perform comprehensive testing tasks. 

In this blog, we will explore prompt engineering with AI, the evolution of AI and quality assurance professionals, and what your team can expect for the future of testing. 

 

What Is Prompt Engineering With AI? 

Prompt engineering in AI is the practice of creating prompts that instruct an AI model or tool on how to perform tasks to achieve a specific outcome. In other words, prompt engineering asks the AI tool the best questions to get the best result. Prompt engineering with AI has a variety of uses, both during testing and afterwards for analyzing test results.  

 

 

As AI advances, testers will increasingly leverage prompt engineering to instruct AI models to generate, execute, and analyze tests for various scenarios. Instead of manually designing tests for different aspects like functional, performance, or accessibility testing, testers will craft precise prompts that guide AI to automate the entire testing process—integrating traditionally siloed testing domains. 

 

 

Using Prompt Engineering With AI to Automate Testing 

Imagine a tester working on a complex enterprise application. Using generative AI, the tester can employ prompt engineering to automate the testing of functional aspects, performance metrics, load capacities, accessibility compliance, and microservices simultaneously.  

Here's how it might work: 

  1. Functional Testing: The tester writes a prompt instructing the AI to simulate typical user journeys, validating form inputs, button clicks, and navigation flows across various devices and browsers. 

Prompt: "Generate test cases to validate our web application's user login, registration, and checkout workflows. Ensure tests cover Chrome, Firefox, and mobile devices (iOS, Android)." 

  1. Performance and Load Testing: In the same testing session, the tester could extend the Prompt to include performance benchmarks, asking the AI to simulate hundreds or thousands of users interacting with the application simultaneously. 

Prompt: "Simulate 500, 1000, and 5000 concurrent users navigating the application, measuring response times for each API endpoint, and generating a load report for all transactions." 

  1. Microservices Testing: The tester can guide the AI to ensure that each microservice responds correctly under various conditions, validating service-to-service communication and API functionality. 

Prompt: "Test the microservice architecture by simulating requests between the user profile, payment, and order processing services. Verify the correctness of data exchange and monitor for any latency." 

  1. Accessibility Testing: The same generative model could be directed to conduct accessibility checks, identifying issues with color contrast, keyboard navigation, and screen reader compatibility in compliance with WCAG standards. 

Prompt: "Check the web application for accessibility compliance, ensuring it meets WCAG 2.1 AA standards for color contrast, keyboard operability, and screen reader compatibility. Generate a report on any violations." 

  1. Integrating All Testing into One Flow: With advanced prompt engineering, a tester can combine these different tasks into a single unified prompt, enabling the AI to perform what traditionally would have required separate testing tools. 

Unified Prompt: "Run a comprehensive test on the web application, covering functional testing for user workflows, performance testing with 1000 concurrent users, load testing with 5000 users, microservices testing for API calls between key services, and accessibility testing according to WCAG 2.1 AA. Provide a summary report with insights on all aspects." 

 

This approach eliminates the need for testers to rely on multiple tools for different testing tasks. Instead, they focus on creating high-level prompts that instruct the AI to perform cross-functional testing more integrated and efficiently. 

 

Related Reading: Learn more about AI in Java IDEs

How Prompt Engineering With AI Evolves the Tester's Role 

Prompt engineering with AI streamlines the testing process in numerous ways. Let us take a closer look at some of the ways in which the tester’s role is bound to evolve in the coming years: 

 

Move from Manual to Strategic Testing 

Testers no longer spend time writing and maintaining detailed test scripts for different tools. Instead, they strategically craft prompts that align with business requirements and technical specifications. 

 

Increased Efficiency  

Testers can cover more ground in a single test cycle, improving test coverage across multiple dimensions without switching between tools. 

 To further streamline execution, you can trigger automated tests using Gradle as part of your CI workflow with Perfecto.

Collaboration With AI 

Rather than fearing job loss, testers will become AI orchestrators, ensuring that the AI-generated tests align with real-world scenarios and business logic. 

 

 

In the future, testers who master prompt engineering will become even more valuable, as they will be vital in leveraging AI's full potential in software testing. The role of the tester will shift from executing manual tests to strategically guiding AI to automate a broader range of testing tasks, integrating functional, performance, load, and accessibility testing into a single, seamless flow. 

 

 

Bottom Line 

As AI continues to take over the testing industry, roles and responsibilities are bound to evolve. By carefully assessing your team’s testing needs, selecting the right AI-powered testing tools, and embracing continuous testing with an eye on the future, teams can harness the power of AI to elevate their software quality and maintain a competitive edge in the market. 

For those tackling system resilience and failure scenarios, combining AI-driven testing with proven approaches like chaos monkey software can further strengthen confidence in production-readiness.

When you embrace the power of AI with Perfecto, your team will experience the following features and benefits: 

  • AI-Powered Root Cause Analysis 

  • AI-Driven Test Execution and Validation 

  • AI-Driven Image Data Generation 

  • Pop-Up Detection 

  • Self-Healing Object Identification 

  • And more! 

 

Experience the industry’s leading AI-driven testing platform first-hand by signing up for a free 14-day trial of Perfecto today.