The advancement of artificial intelligence raises two intriguing questions about our discipline:
- How do we use artificial intelligence to make quality validation smarter?
- How do we easily and effectively validate AI solutions?
This report aims to assist you in understanding the potential of AI and how it can help improve the quality, velocity, and efficiency of your quality engineering activities. In partnership with leading technology providers, we provide specific suggestions, ideas, and examples that can help you address the first question. We will return to you with answers to the second question in a subsequent series.
We will dive into the different quality assurance practices, illustrating each with specific use cases:
- Section 1 delves into the fundamentals of the QE and AI convergence,
- Section 2 discusses how artificial intelligence can be used to address some of the challenges associated with test design,
- Section 3 looks at ways to improve the decision-making process through 2 sub-sections:
- Section 3.1 proposes a journey towards smart analytics
- Section 3.2 demonstrates how we might derive meaningful insights from unstructured data,
- Section 4 examines how to further automate functional testing through 2 sub-sections:
- Section 4.1 discusses how Computer Vision might help us overcome our test automation issues
- Section 4.2 dives into how AI helps us reach higher level of reliable automation,
- Section 5 reviews how AI can be used to address test data challenges,
- Section 6 addresses the role of AI in performance engineering,
- Section 7 focuses on how artificial intelligence can improve security testing,
- Section 8 discusses the role of AI in enhancing IT Operations,
- Section 9 examines how AI and ethics will continue to influence QE,
- The concluding section will provide a prediction about the future of our discipline.
We wish you an excellent read.